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With the development of e-commerce logistics in China, online shopping has become increasingly popular among consumers, and the fresh e-commerce industry has shifted to a pre-positioning warehouse distribution model closer to consumers. This study starts from consumer demands and considers the spatial distribution characteristics of consumers. Through cluster analysis, demand points with high consumer density are identified, and potential locations are determined through two rounds of clustering. Drawing insights from the warehousing and distribution models of Freshippo and MissFresh in Wuhan and Nanjing, four classification algorithms are compared to determine the optimal pre-positioning warehouse distribution model for the enterprise in this study. A multi-objective optimization model is established, aiming to minimize enterprise costs and maximize customer satisfaction, and the optimal site selection plan is obtained. By determining the number and locations of pre-positioning warehouses through the optimal solution, the enterprise can achieve maximum investment returns in the spatial layout of these warehouses.

With the continuous improvement of digitization, the processing and analysis of massive data has become one of the hot issues. Soft computing technology, as an emerging machine intelligence technology, performs well in handling complex uncertainty problems and is an important component of artificial intelligence. This study takes soft computing technology as the technical core and constructs a fuzzy dynamic clustering model based on improved immune algorithms to process unlabeled data. And an anomaly detection and analysis algorithm is designed based on soft instance transfer learning to handle weakly labeled data. The performance test outcomes denote that the accuracy, recall, and F1 values of the immune optimization fuzzy dynamic clustering algorithm are 91.69%, 89.27%, and 92.15%, respectively, reaching the optimal level of similar intelligent optimization clustering algorithms. The immune optimization fuzzy dynamic clustering algorithm has better computational efficiency, loss function curve performance, and strong global search ability, and avoids the occurrence of local optimal solutions. Compared with other advanced clustering algorithms, the immune optimization fuzzy dynamic clustering algorithm performs well on datasets in various fields, and both external and internal evaluation indicators verify the algorithm’s clustering effect. The AUC value of the soft computing instance transfer learning anomaly detection algorithm is 0.913, with a detection accuracy of 91.67%, which is superior to other anomaly detection algorithms. The unlabeled and weak-label data processing model designed based on soft computing technology can effectively achieve the processing and analysis of real-world data problems.

Checking receipt data is essential for tax authorities to ensure compliance with financial reporting regulations, identify potential anomalies, and prevent tax violations. This study explores the application of genetic algorithms (GA) for hyperparameter optimization (HPO) in Isolation Forest (IF) and Autoencoder (AE) models with objective to enhance anomaly detection (AD) with focus on identifying cash registers (CRs) that display patterns closely resembling known anomalies, thereby improving AD capabilities. The dataset, comprising 2.7 million receipt records, encompassing transactional details such as payment amounts, dates, and the ratio of cash to cashless transactions. As anomalies were identified on CR level, the receipt dataset was underwent feature engineering aggregated by cash and obtained included 40,715 CRs, with 200 known anomalies. The primary goal was to accurately identify existing anomalies and those closely resembling them, which are referred to as false positives (FP) obtained by the best optimized IF and AE models, as comprehensively as possible. The best-performing IF model on a test set of 20,375 CR records with 100 known anomalies correctly identified 25 anomalies and flagged 999 similar (FP) patterns. The AE model, tested on 8,154 records with 39 known anomalies, correctly identified 15 and flagged an additional 437 CRs (FP). Subsequent clustering analysis of FP and existing anomalies - true positives (TP) reveals nuanced restaurant transactional patterns suggestive of potential tax evasion, aiding in strategic decision-making regarding tax authority inspections.

The privacy protection problem in data mining has received increasingly attention and is a hot topic of current research. To address the problems of large accuracy loss and instability of clustering results of clustering algorithms under differential privacy protection requirements, a density peak clustering algorithm for differential privacy protection (DP-chDPC) is proposed. Firstly, the original DPC algorithm is improved, by using the dichotomy method to automatically determine the truncation distance to avoid the subjectivity of manual selection, and by setting the threshold of local density and center offset distance to automatically obtain the clustering center, which overcomes the uncertainty of the original DPC algorithm to select the clustering center based on the decision graph. Then, noise is added to the local density by using the Laplace mechanism to realize the differential privacy protection of the algorithm during the clustering analysis. Finally, the Chebyshev distance is used to replace the Euclidean distance to calculate the distance matrix, which reduces the interference on the clustering results after the algorithm adds noise, and reduces the loss of clustering accuracy, so that the stability of the algorithm is improved. The experimental results show that the DP-chDPC algorithm can effectively reduce the loss of clustering accuracy after the algorithm adds noise, and the clustering results are more stable.

In Physics Higher Education (PHE), Student Evaluation of Teaching (SET) surveys are widely used to collect students’ feedback on courses and instructions. In our research, we propose a more efficient way to summarize students’ free responses from the Student Assessment of their Learning Gains (SALG) survey, a form of the SET survey, of an algebra-based introductory physics course at a large Canadian research university. Specifically, we use cluster and sentiment analysis methods such as K-means and Valence Aware Dictionary for sEntiment Reasoning (VADER) to summarize students’ free responses. For cluster analysis, we extract popular keywords and summaries of responses in different clusters that reflect students’ dominant opinions toward each aspect of the course. Notably, we obtain an average silhouette coefficient of 0.480. In addition, we analyze sentiments in students’ free responses that are determined through applying VADER. Intriguingly, we see that VADER (micro F1 = 0.57, macro F1 = 0.55) can better classify responses with positive (F1 = 0.62) and neutral sentiment (F1 = 0.59). However, evident disagreements arise with negative sentiment responses (F1 = 0.42). In addition, our research suggests that some Likert-scale summaries deviate from the sentiment of free response summaries due to the limitations of Likert-scale responses. By creating various visualizations, we discover that Natural Language Processing (NLP) methods, such as cluster and sentiment analysis, effectively summarize students’ free responses, with several limitations.

The K-means algorithm, widely used in cluster analysis, is a centroid-based clustering method known for its high efficiency and scalability. However, in realistic situations, the operating environment is susceptible to contamination issues caused by outliers and distribution departures, which may lead to clustering results from K-means that are distorted or rendered invalid. In this paper, we introduce three other alternative algorithms, including K-weighted-medians, K-weighted-L2-medians, and K-weighted-HLs, to address these issues under the consideration of data with weights. The impact of contamination is investigated by examining the estimation effects on optimal cluster centroids. We explore the robustness of the clustering algorithms from the perspective of the breakdown point, and then conduct experiments on simulated and real datasets to evaluate their performance using two new numerical metrics: relative efficiencies based on generalized variance and average Euclidean distance. The results demonstrate the effectiveness of the proposed K-weighted-HLs algorithm, surpassing other algorithms in scenarios involving both contamination issues.

Hand grasp patterns are the results of complex kinematic-muscular coordination and synergistic control might help reducing the dimensionality of the motor control space at the hand level. Kinematic-muscular synergies combining muscle and kinematic hand grasp data have not been investigated before. This paper provides a novel analysis of kinematic-muscular synergies from kinematic and EMG data of 28 subjects, performing 20 hand grasps. Kinematic-muscular synergies were extracted from combined kinematic and muscle data with the recently introduced Mixed Matrix Factorization (MMF) algorithm. Seven synergies were first extracted from each subject, accounting on average for ${>}75$ % of the data variation. Then, cluster analysis was used to group synergies across subjects, with the aim of summarizing the coordination patterns available for hand grasps, and investigating relevant aspects of synergies such as inter-individual variability. Twenty-one clusters were needed to group the entire set of synergies extracted from 28 subjects, revealing high inter-individual variability. The number of kinematic-muscular motor modules required to perform the motor tasks is a reduced subset of the degrees of freedom to be coordinated; however, probably due to the variety of tasks, poor constraints and the large number of variables considered, we noted poor inter-individual repeatability. The results generalize the description of muscle and hand kinematics, better clarifying several limits of the field and fostering the development of applications in rehabilitation and assistive robotics.

Traditional deep neural network (DNN) based process monitoring methods only use the deep features of the last layer and residuals to achieve fault detection. However, the features in different hidden layers are different representations of the input data, which may be beneficial to process monitoring. Only using the deepest features for process monitoring will cause the problems of information loss and low monitoring performance. To obtain more useful information for fault detection, this paper considers the features in all hidden layers and proposed an ensemble monitoring model based on multi-subspace partition of deep features. Firstly, a DNN model is established based on the collected faultless data to obtain the features in all hidden layers and residuals. Secondly, a new feature matrix is constructed based on the retained deep features and residuals. Then, the multi-subspace partition of the new feature matrix is realized by combining correlation analysis and cluster analysis. Finally, the monitoring statistics that are established based on the features in each subspace are fused to realize process monitoring. The proposed method can not only reduce information loss but also enrich the fault-related information. The monitoring performance is verified through two benchmark processes and one actual industrial process.

The rapid development of internet technology has brought convenience to people’s lives, increasingly making their work and personal activities inseparable from the internet. However, the various types of information data in the Internet are very large, and accurately obtaining useful information from them has become particularly important. Therefore, clustering analysis technology is widely applied in various fields. The elastic network algorithm is vulnerable to noise and other factors in clustering analysis, resulting in low accuracy of clustering results. To solve this problem, this study proposes an elastic network algorithm based on weighted properties for clustering analysis. After comparative experiments, the results showed that the accuracy of the proposed algorithm was 98%. The area under the precision recall curve was 0.86. The maximum F-value was 0.93. The area under the working characteristic curve of the subject was 0.94. The precision ultimately stabilized at 97%. All values were higher than the comparison algorithm. In the experiment, the running time of the algorithm was 11 seconds and the average error was 7%, both of which were lower than the comparison algorithm. When the algorithm was affected by Statistical dispersion and threshold parameters, the accuracy of the proposed algorithm was 71%, 69% and 67% respectively, which were better than the comparison algorithm. In conclusion, the proposed elastic network algorithm based on the weighted characteristics has high accuracy, strong stability and advantages in clustering.

Rainfall prediction plays a crucial role in raising awareness about the potential dangers associated with rain and enabling individuals to take proactive measures for their safety. This study aims to utilize machine learning algorithms to accurately predict rainfall, considering the significant impact of scarcity or extreme rainfall on both rural and urban life. The complex nature of rainfall, influenced by various atmospheric, oceanic, and geographical factors, makes it a challenging phenomenon to forecast. This research employs data preprocessing techniques, outlier analysis, correlation analysis, feature selection, and several machine learning algorithms such as Naive Bayes (NB), Decision Tree, Support Vector Machine (SVM), Random Forest, and Logistic Regression. The study focuses on developing the most accurate rainfall prediction model by utilizing machine learning and feature selection techniques. The Artificial Neural Network (ANN) achieves a maximum accuracy of 90% and 91% before and after feature selection, respectively. Furthermore, k-means clustering and Principal Component Analysis (PCA) are applied to examine regional rainfall patterns in Australia. Lastly, to make our proposed machine learning simpler and more usable for general people, we have formulated a web-based application system using Flask in our research paper. Overall, this research demonstrates the effectiveness of different machine-learning techniques in predicting rainfall using Australian weather data.

The problem of characterizing the passengers’ movement in a public transport system has been considered in the literature for analysis, simulation and optimization purposes. In particular, origin-destination matrices are commonly used to describe the total number of passengers that travel between two points during a given time interval. In this paper, we propose to model the instantaneous rate of arrival of passengers for the origin-destination pairs of a metro system using point processes. More specifically, we apply the Expectation-Maximization algorithm to estimate the parameters of a Gaussian mixture intensity function for the daily flow of passengers using data from multiple days provided by EFE Valparaíso. The uncertainty in the parameter estimates is quantified computing standard errors and confidence intervals. Secondly, we quantitatively analyze the similarity of the obtained intensity functions among the different origin-destination pairs. In particular, we propose a dissimilarity index based on the Kullback-Leibler divergence and we apply this index in hierarchical agglomerative and partitioning methods to cluster origin-destination pairs with similar daily flow of passengers. The obtained numerical results confirm expert knowledge about the passengers’ behavior in EFE Valparaíso metro system and, more interestingly, provide additional insights on the passengers’ behaviour for specific origin-destination pairs.

Electronic commerce (EC) has become the most critical business activity worldwide. China has become the world’s second largest economy. This study reviewed EC studies in China because although research on EC has yielded numerous results, limited research have reviewed these papers. The data used in this study were obtained from the Web of Science database. A total of 1,982 journal articles published between 2001 and 2020 were collected. In addition to conducting an overall analysis on EC in China, this study referred to the Five-Year Plan for Economic and Social Development of the People’s Republic of China and divided the research period into 5-year stages. The BibExcel, UCINET, and SPSS software programs were used to conduct co-word analysis on the keywords in the papers for determining the knowledge structure and clustering of each stage and understanding the trend of leading research in the future. The results indicated that (a) Stages I–IV comprised 4, 3, 4, and 4 clusters, respectively. (b) Consumer’s personalized demands were considered in EC development activities ranging from the initial EC infrastructure construction to the integration of artificial-intelligence-related technology. (c) Topics regarding consumer behaviors were centered on Stages II and III, which indicated that the research on these topics was mature. (d) Stage IV explored the new research topic of integrating smart technology into the EC environment and indicated the characteristics of the e-market.

The number of scholarly publications has dramatically increased over the last decades. For anyone new to a particular science domain it is not easy to understand the major trends and significant changes that the domain has undergone over time. Temporal summarization and related approaches should be then useful to make sense of scholarly temporal collections. In this paper we demonstrate an approach to analyze the dataset of research papers by providing a high level overview of important changes that occurred over time in this dataset. The novelty of our approach lies in the adaptation of methods used for semantic term evolution analysis. However, we analyze not just semantic evolution of single words independently, but we estimate common semantic drifts shared by groups of semantically converging words. As an example dataset we study the ACL Anthology Reference Corpus that spans from 1974 to 2015 and contains 22,878 scholarly articles.

This paper addresses the problem of fuzzy clustering for categorical data. During the last two decades, many attempts have been made to extend the $k$ -means algorithm, making it applicable to clustering for categorical data, due to its simplicity and efficiency. However, as $k$ -means-like algorithms are local optimization methods, their clustering results are highly sensitive to initialization. In this paper, we propose to use Locality-Sensitive Hashing (LSH) to reduce the categorical data dimensions and predict the initial fuzzy clusters in low-dimensional space. Different from the existing cluster initialization optimization methods that aim to create only crisp initial clusters, the proposed method aims at predicting ‘high quality’ fuzzy clusters at the initialization step before proceeding in the $k$ -means-like fashion. The numerical results show that the proposed method yields relatively accurate results on 16 UCI datasets and outperforms all other related approaches in terms of both crisp and fuzzy clustering effectiveness.

This article presents the application of cluster analysis (CA) to data proceeding from a testbed microgrid located at Sapienza University of Rome. The microgrid consists of photovoltaic (PV), battery storage system (BESS), emergency generator set, and different types of load with a real-time energy management system based on supervisory control and data acquisition. The investigation is based on the area-related approach - the CA algorithm considers the input database consisting of data from all measurement points simultaneously. Under the investigation, different distance measures (Euclidean, Chebyshev, or Manhattan), as well as an approach to the optimal number of cluster selections. Based on the investigation, the four different clusters that represent working conditions were obtained using methods to define an optimal number of clusters. Cluster 1 represented time with high PV production; cluster 2 represented time with relatively low PV production and when BESS was charged; cluster 3 represents time with relatively high PV production and when BESS was charged; cluster 4 represents time without PV production. Additionally, after the clustering process, a deep analysis was performed in relation to the working condition of the microgrid.

Increased safety is one of the main motivations for traffic research and planning. The arduous task has two components: (i) improving the existing traffic policies based on a good understanding of risk factors related to trends in traffic accidents, and (ii) underpinning the emerging technologies that will advance the safety of vehicles. For the latter route, the introduction of connected and automated vehicles (CAVs) is a promising option as CAVs can potentially reduce the number of accidents. However, to reap their benefits, they need to be introduced in a safe manner and tested for their ability to safely deal with risky scenarios. Unfortunately, the identification of such test scenarios remains a key challenge for the industry. This study contributes to increased safety by (i) analyzing UK’s STATS19 accident data to identify patterns in past traffic accidents, and (ii) utilizing this information to systematically generate scenarios for CAV testing. For task (i), the patterns in the accidents were identified in terms of static and time-dependent internal and external factors. For this purpose, the study employed a clustering algorithm, COOLCAT, which is particularly suitable for dealing with high-dimensional categorical data. Six different clusters emerged naturally as a result of the algorithm. To interpret the clusters, we applied a frequency analysis to each cluster. The frequency tests showed that in each cluster, certain distinct real-world situations were represented more significantly compared to the non-clustered reference case, which are the markers of each cluster. The second task (ii) complemented the first task by synthesizing the relationships between attributes. This was done by association rule mining using the market basket analysis approach. The method enabled us to develop, drawing from the characteristics of the clusters, non-trivial test scenarios that can be used in the testing of CAVs, especially in virtual testing.

Uncertainty measurement (UM) gives a brand-new perspective on attribute reduction in an information system (IS). Interval-valued data is a kind of very vital data in rough set theory (RST). Rough set model based on tolerance relations can be considered to deal with interval-valued data. However, these kinds of tolerance relations have deficiencies when they are used in fuzzy rough computation. This paper studies new UM for an interval-valued information system (IVIS) and considers its attribute reduction. Firstly, a novel fuzzy symmetry relation on the object set of an IVIS is established based on “The similarity between information values that is fed back to the attribute set”. Secondly, $\lambda $ -information granules on the basis of a fuzzy symmetry relation are obtained. Then, four UMs for an IVIS are investigated. Next, numerical experiments and statistical tests are used to evaluate the performance of the proposed UMs. Moreover, attribute reduction in an IVIS is studied and the relevant algorithms are proposed. Finally, clustering analysis on the reduced IVIS is conducted. Experimental results indicate that the proposed algorithms are effective based on evaluation indicators of clustering performance. This paper provides a novel viewpoint for the establishment of fuzzy symmetry relation and attribute reduction algorithms.

Since an intensely subjective expert weights and the divergence of opinions among experts exist in the evaluation, this paper proposes a modified multi-expert combined weight (MMCW)-fuzzy comprehensive evaluation (FCE) method. This algorithm preliminarily screened the expert-weight set based on the cluster analysis. After designing the objective function according to the information entropy and relative entropy theory, the multi-expert combined-weight single-objective optimization model was constructed via the weighting coefficient. However, the solution happens to poor convergence in the traditional intelligent algorithm, easily falling into the local optimum. Thus, a differential brain storm optimization (DBSO) algorithm was exploited to figure the established optimal model out for the combined coefficient, and the result was obtained via the hierarchy fuzzy comprehensive evaluation. As taken the AUV as an example, the three-layer evaluation index system was established from the motion control, target recognition, and path planning to assess the AUV intelligence, and the simulations were conducted to validate the proposed algorithm. Conclusively, the MMCW-FCE can quantitively evaluate the intelligence of diverse AUV systems on the algorithm level.

The COVID-19 crisis has attracted attention worldwide to supply chain disruptions and resilience. Several supply chain risk management approaches have been revisited or reapplied in the literature; however, collaborative resource sharing is less researched. This study aimed to investigate the current academic state of the art and advances in using collaborative resource sharing as a reactive method to facilitate supply chain recovery in the presence of disruptive events. More specifically we considered the role of different collaborative resource-sharing strategies that organizations can adopt to support supply chain functionalities during times of disruption. We conducted a systematic literature review (SLR) to analyze academic articles that were published online from 2000 to 2022. In order to analyze the literature, we adopted a combination of text-mining, automatic and manual categorization of selected articles, and exploratory analyses such as cluster analysis and relational indicators. We also consider the machine learning classification algorithm i.e. agglomerative hierarchical clustering for the categorization of clusters. The findings show that, for disruptive risks, collaborative sharing of labour and material resources is effective for the recovery of supply chains. More so, labour resources tend to contribute more to the recovery of supply chains through the physical and mental recreation of recovery activities and experiences. Whilst information resources and a mix of information and material resources are highly important in reducing the impact of COVID-19 disruptive supply chain risk. In conclusion, collaborating on the three resources, namely labour, material, and information resources can be an effective post-disruption recovery strategy for supply chains.

The fifth-generation (5G) of networks is being deployed to provide a wide range of new services and to manage the accelerated traffic load of the existing networks. In the present-day networks, data has become more noteworthy than ever to infer about the traffic load and existing network infrastructure to minimize the cost of new 5G deployments. Identifying the region of highest traffic density in megabyte (MB) per $\text {km}^{2}$ has an important implication in minimizing the cost per bit for the mobile network operators (MNOs). In this study, we propose a base station (BS) clustering framework based on unsupervised learning to identify the target area known as the highest traffic cluster (HTC) for 5G deployments. We propose a novel approach assisted by real data to determine the appropriate number of clusters $k$ and to identify the HTC. The algorithm, named as NetClustering, determines the HTC and appropriate value of $k$ by fulfilling MNO’s requirements on the highest traffic density $\text {MB/km}^{2}$ and the target deployment area in $\text {km}^{2}$ . To compare the appropriate value of $k$ and other performance parameters, we use the Elbow heuristic as a benchmark. The simulation results show that the proposed algorithm fulfills the MNO’s requirements on the target deployment area in $\text {km}^{2}$ and highest traffic density $\text {MB/km}^{2}$ with significant cost savings and achieves higher network utilization compared to the Elbow heuristic. In brief, the proposed algorithm provides a more meaningful interpretation of the underlying data in the context of clustering performed for network planning.

Test suite reduction is an effective way to reduce the cost of regression testing by identifying and removing redundant test cases from the original test suite. In this paper, we propose a novel cluster test suite reduction using an evolutionary multi-objective optimization algorithm. Specifically, we use a K-means algorithm to group similar test cases to the same cluster. Then the evolutionary algorithm is used to remove redundant test cases based on the clustering results, and optimization objects are represented as the coverage-related criteria, fault-related criteria and cost-related criteria. The experimental results involving eight subject programs show that the proposed method can outperform the other three state-of-the-arts with respect to both fault detection (4.61% -9.44%) and reduction ratio (4.10% -10.64%). Meanwhile, the experiments also prove that our method has a better performance of missing failure rate (0.049% -0.132%) and code coverage rate (3.34% -6.10%). Besides, the proposed method costs are found to be comparable to the other techniques.

In this paper, we propose a coherent framework for multi-machine analysis, using a group clustering model, which can be utilized for predictive maintenance (PdM). The framework benefits from the repetitive structure posed by multiple machines and enables for assessment of health condition, degradation modeling and comparison of machines. It is based on a hierarchical probabilistic model, denoted Gaussian topic model (GTM), where cluster patterns are shared over machines and therefore it allows one to directly obtain proportions of patterns over the machines. This is then used as a basis for cross comparison between machines where identified similarities and differences can lead to important insights about their degradation behaviors. The framework is based on aggregation of data over multiple streams by a predefined set of features extracted over a time window. Moreover, the framework contains a clustering schema which takes uncertainty of cluster assignments into account and where one can specify a desirable degree of reliability of the assignments. By using a multi-machine simulation example, we highlight how the framework can be utilized in order to obtain cluster patterns and inherent variations of such patterns over machines. Furthermore, a comparative study with the commonly used Gaussian mixture model (GMM) demonstrates that GTM is able to identify inherent patterns in the data while the GMM fails. Such result is a consequence of the group level being modeled by the GTM while being absent in the GMM. Hence, the GTM are trained with a view on the data that is not available to the GMM with the consequence that the GMM can miss important, possibly even key cluster patterns. Therefore, we argue that more advanced cluster models, like the GTM, can be key for interpreting and understanding degradation behavior across machines and ultimately for obtaining more efficient and reliable PdM systems.

Real-world datasets, particularly Electronic Health Records, are routinely found to be mixed (comprised of both categorical and continuous variables) and/or missing in nature. Such datasets present peculiar challenges related both to their clustering and the evaluation of the clusterings obtained. In this paper, we discuss these challenges in detail, as well as the solution approaches applied to them in the literature. We then apply some of these approaches to a multi-racial Chronic Kidney Disease (CKD) dataset comprising of 20 continuous and 12 categorical variables with an over 30% missingness ratio, evaluating our results through external and internal validation as well as cluster stability testing. From the results of our study, the Ahmad-Dey distance measure consistently outperformed Gower’s distance on our mixed and missing dataset. In addition, our results show that advanced imputation methods like multiple imputation, which take into consideration the uncertainty inherent in imputation, should be explored when clustering missing datasets. Three clusters were identified from our dataset which were significantly differentiated by age, sex, estimated Glomerular Filtration Rate (eGFR), creatinine, urea, and hemoglobin, but not by race or blood pressure. The fact that, through proper cluster analysis, we were unable to identify five clusters corresponding to the five CKD stages usually used to classify CKD patients indicates that datasets with more than the usual four/six variables used for computing eGFR may contain a latent structure different from this five-group structure, the identification of which will provide valuable insights peculiar to each cohort for medical practitioners.

In the context of cluster analysis and graph partitioning, many external evaluation measures have been proposed in the literature to compare two partitions of the same set. This makes the task of selecting the most appropriate measure for a given situation a challenge for the end user. However, this issue is overlooked in the literature. Researchers tend to follow tradition and use the standard measures of their field, although they often became standard only because previous researchers started consistently using them. In this work, we propose a new empirical evaluation framework to solve this issue, and help the end user selecting an appropriate measure for their application. For a collection of candidate measures, it first consists in describing their behavior by computing them for a generated dataset of partitions, obtained by applying a set of predefined parametric partition transformations. Second, our framework performs a regression analysis to characterize the measures in terms of how they are affected by these parameters and transformations. This allows both describing and comparing the measures. Our approach is not tied to any specific measure or application, so it can be applied to any situation. We illustrate its relevance by applying it to a selection of standard measures, and show how it can be put in practice through two concrete use cases.

In the field of medical, each and every analysis is decisive as the study links to life of the subject under observation. One of the most vital area in the field of medical is the healthcare of expecting women in low income countries. High mortality rate due to increased number of caesarean section is evident because of poor medical infrastructure in the region, misunderstood religious teachings, low education and lack of proper decision making at the right time. The root cause analysis of situations demanding caesarean section is a tough job, however in the presence of historical data, one may extract useful information that will help supporting a medical decision by predicting the outcome. It is obvious that regional disparities have a huge impact on the residents of that region. A study performed on any region cannot be all applicable to the residents of some other distant region. This motive has established grounds to conduct a local study upon the data collected from expecting women in city Muzaffarabad, Kashmir. It is believed that the findings of this study will be significant for women that share more or less similar physical, social and maternal traits. Keeping this in mind, study presents an analysis of two clustering techniques for the investigation of appropriate algorithm that groups data into relevant clusters robustly. Firstly, we analyzed K-means and K-medoids algorithms' capability to cluster the data using different distance metrics. Secondly, data transformation techniques including scale, range and Yeo-Johnson are applied. Finally, transformed data are used in K-means and K-medoids algorithms' to generate cluster accuracy. It is observed that the results produced from transformed data are better than using raw data. Yeo-Johnson transformation method is found best for k-means (Hartigan & Wang), K-medoids (SEV distance function) and Rank k-medoids (SEV distance function) with mean accuracy 67.58%, 69.58% and 72.64% respectively.

Sequence classification is an important data mining task in many real-world applications. Over the past few decades, many sequence classification methods have been proposed from different aspects. In particular, the pattern-based method is one of the most important and widely studied sequence classification methods in the literature. In this paper, we present a reference-based sequence classification framework, which can unify existing pattern-based sequence classification methods under the same umbrella. More importantly, this framework can be used as a general platform for developing new sequence classification algorithms. By utilizing this framework as a tool, we propose new sequence classification algorithms that are quite different from existing solutions. Experimental results show that new methods developed under the proposed framework are capable of achieving comparable classification accuracy to those state-of-the-art sequence classification algorithms.

In spatial-temporal data analysis, location data and its evolution through time are investigated with the goal of uncovering important information to provide novel insights. These insights, for example, may involve congestion identification in transportation, mobility patterns in urban computing, and storm prediction in weather forecasting. Clustering, one data analysis technique, groups spatial-temporal data based on location. Current spatial-temporal data analysis techniques fail to investigate relationships between spatial-temporal clusters, such as splitting from a cluster and merging with another one because of a change of properties over time. These relationships could hold valuable information about the existence of a cluster and its interactions with other clusters and trajectories. In this paper, we introduce a framework to identify, process, and analyze relationships between clusters of spatial-temporal data (e.g. enter, merge, or split). We describe its architecture and components, as well as a proposed clustering technique, the different approaches for distance calculation, and how we calculate cluster similarity of temporally separated clusters. The result of these operations are used in the identification of cluster relationships over space and time. The analysis of these relationships helps uncover hidden values that could support novel approaches to more effective decision-making. We evaluate our framework with two case studies, based on truck and human trajectories.

In this paper, we propose a novel method of cluster analysis called unsupervised functional link artificial neural networks (UFLANNs), which inherit the best characteristics of functional link artificial neural networks and self-organizing feature maps (SOFMs). UFLANNs adopt three types of basis functions such as Chebyshev, Legendre orthogonal polynomials, and power series for mapping the input data into a new feature space with higher dimensions, where the objects are clustered based on the principle of competitive learning of SOFMs. The effectiveness of this algorithm has been tested with various artificial and real-life datasets including remote sensing images. A thorough comparison with other popular clustering algorithms shows that the proposed method is promising in revealing clusters from many complex datasets.

Due to the rapid development of information technology and network technology, there is a lot of data, but the phenomenon of lack of knowledge is becoming more and more serious. Data mining technology has developed vigorously in this environment, and it has shown more and more vitality. Based on Spark programming model, this paper designs the parallel extension of fuzzy c-means. In order to enhance the performance of fuzzy c-means parallel expansion, the improvement strategy of k-means during the initialization phase is borrowed, and k-means// is extended to fuzzy c-means to obtain better clustering performance. Combined with Spark's programming model, this paper can obtain extended parallel fuzzy c-means algorithm. Several experiments on the data set of the algorithm proposed in this paper have shown good scalability and parallelism, effectively expanding fuzzy c-means clustering to distributed applications, greatly increasing the scale of the data processed by the algorithm. This improves the robustness of the algorithm and the adaptability of the algorithm to the shape and structure of the data, so that the parallel and scalable clustering algorithm can more effectively perform cluster analysis on big data. Three algorithms were simulated on MATLAB platform. We use simple data sets and complex two-dimensional data sets, and compare with the traditional fuzzy c-means algorithm and fuzzy c-means algorithm based on fuzzy entropy. Experiments show that the scalable parallel fuzzy c-means algorithm not only greatly improves the anti-noise performance, but also improves the convergence speed, and it can automatically determine the optimal number of clusters.

This paper proposes an automatic fall detector in a wearable device that can reduce risks by detecting falls and promptly alerting caregivers. For this purpose, we propose cluster-analysis-based user-adaptive fall detection using a fusion of heart rate sensor and accelerometer. The objectives of the proposed fall detector are to have high accuracy with a low-complexity model regardless of diverse conditions. To meet the objectives, we propose the best 13-dimensional feature subset by using feature selection. In addition, we verify the performance increment of combining a heart rate sensor with an accelerometer and the effectiveness of the cluster-analysis-based anomaly detection. We also show the effectiveness of the user-adaptive method when using both heart rate and acceleration signals that were hardly covered in other papers. Finally, we prove that the performance of the proposed fall detector achieves is better than that of recent user-adaptive and user-independent approaches. This study is the first attempt to demonstrate the merits of the user-adaptive approach using a combination of heart rate and acceleration signals to detect falls. Moreover, this paper also contributes to fall detection area by providing the data we collected.

This article first defines a hidden Markov linear regression model for the purpose of further studying the mutual transformation between different states in the linear regression model, and the regression relationship between the dependent variable and the independent variable in each state. And then, K-means clustering analysis methods are used to identify the hidden states of observed data, and the maximum likelihood estimation of the hidden state transition probability matrix elements is obtained by using the maximum likelihood estimation method, and parameter estimation of unknown parameters in linear regression model is also presented by using the least squares method. Finally, the observation vector set is generated according to the defined model, and the empirical simulation demonstrates that the parameter estimation method shown in this work is reliable.

In the current electrical load profile analysis, considering the shortage of traditional methods on the typical load profile extraction of single consumers and the load profile feature extraction, this paper proposes an approach based on time series data mining. Firstly, this method reduces the dimension of the load profile of a single consumer based on the Piecewise Aggregate Approximation(PAA), and re-expresses the load profile of the consumer over a period based on the Symbolic Aggregate approXimation(SAX), representing the consumer's load profile with a symbolic string to extract the typical load profile. Then, combined with the load characteristic indices and time series-based features, the typical load profiles of different consumers are clustered based on the K-means algorithm to analyze the power consumption behaviors. Finally, this paper performs a case analysis with a UCI test data set, and the results show that the proposed approach can excavate typical power consumption behaviors of consumers and improve the electrical load profile analysis efficiency and the clustering quality.

Observers with normal color vision have different color perceptions since their retinal cones have different spectral responses, especially when the color pairs are constituted by different primary colors. In order to categorize the color-matching functions (CMFs) for young and aged observers, seven devices with different primary colors were performed on the clustering of observers' CMFs. Four CMFs were generated from Beijing Institute of Graphic Communication (BIGC) with paired-comparison color matching experiments based on five printed colors from young and aged observers performed under fluorescent light source (Experiment I). Besides, color comparison experiments based on three printed colors under light emitting diodes (LEDs) light source were also carried out (Experiment II). And then, the existing CMFs, including Commission Internationale de l' Eclairage 1964 (CIE 1964), CIE 1989 standard deviate observer (CIE 1989 SDO), CIE 2006, Sarkar 1 to Sarkar 8, Asano 1 to Asano 10, BIGC 1 to BIGC 4 were tested with the results from Experiments I and II respectively. The results indicated that the visual data from young and aged observers had quite large discrepancies. When they viewed printed materials illuminated by fluorescent and LED light sources in a field of view larger than 10°, based on the standardized residual sum of squares (STRESS) values and the number of the observers contributed to the minimum STRESS values, two new CMFs were proposed to represent the young and aged observers who participated in our experiments.

This study aims at investigating drivers' risk perception ability. To achieve this objective, the risk sensitivity and risk judgment thresholds of drivers of different ages were calculated. Five scenarios of intersections with risks were established for the driving simulator experiment. The driving behavior data of fourteen younger drivers and fourteen elderly drivers during the risky event and the subjective evaluation of risk by an expert driver were collected. The expert driver's conflict degree and subjective feeling were combined to classify the risk level of driving scene; then, fuzzy signal detection was used to calculate the driver's risk sensitivity (d') and judgment threshold (β). The β with the greatest difference was selected for cluster analysis and drivers were divided into four types according to the threshold. Finally, a driver classification discriminant model was constructed based on Fisher discriminant analysis. The results show that d' and β of younger drivers are both better than those of elderly drivers, younger drivers can detect and respond risks in time, while elderly drivers need be closer to risks and have intuitive feelings to make judgments. The results of the cluster analysis showed that younger drivers account for a large proportion of the sensitive type, indicating that younger drivers can find risks more sensitively in risk scenarios than elderly drivers, while elderly drivers easily ignore risks due to physical and psychological weakness. The correlation analysis showed that age, saccade amplitude and heart rate are the main factors that affect the risk judgment threshold.

Short-term traffic flow forecasting has always been an interesting research at the fields of Intelligent Transportation Systems. This paper presents a time-based combined traffic flow prediction model based on field data collected by loop detectors at signalized intersections, which are used to signal optimization, route choice, traffic monitoring, etc. Firstly, the traffic flow and corresponding travel speed by hour is processed for error elimination and correlation analysis. Secondly, time of day is divided into three groups (peak, flat-peak and low-peak period) in terms of hourly travel speed clustering such as to separately develop prediction formula for each period with avoiding the overfitting of a single 24-hour model. And then, a combined prediction model based on time partition is proposed for 24-hour traffic flow forecasting, which adopts grey theory model for flat-peak and low-peak periods and back-propagation artificial neural network for peak hours, respectively. Finally, in tests that used field data from Xingzhong Rd, Zhongshan, China, the developed combined method based on speed clustering shows promise in reducing mean absolute error, mean absolute percentage error and mean squared error. Further exploration with excessive experiments for comparison analysis exhibits that the period-specific combined model conducts a more accurate and reliable prediction than the individual model and existing combined ones with the same structure for 24-hour.

Over the last decade, the advent of the cluster ensemble framework has enabled more accurate and robust data analysis than traditional single clustering algorithms. The improved clustering of microarray data has had a particularly strong impact in the fields of genomics and medicine. However, when we bring several ensemble members together to form a consensus, low-quality data partitions can seriously compromise the final solution. One way to overcome this problem is the weighted cluster ensemble approach based on Partition Relevance Analysis (PRA), which uses internal cluster validity indices to evaluate and weight the ensemble members before the fusion. Unfortunately, the selection of appropriate validation indices for given data is far from trivial. In this paper, we propose an additional step in PRA that reduces the size of the committee of cluster validation indices. It does so by eliminating redundant and noisy indices using data dimensionality reduction methods. Our extension works in an unsupervised way, minimizing the amount of user intervention and required expert knowledge. We adapted three conventional consensus functions based on the principle of evidence accumulation to work with PRA weights. We demonstrate the advantages of the proposed reduction step of PRA based on extensive experiments with 25 gene expression and 15 non-genetic real-world datasets, where we compared 15 consensus functions. The source code is available at https://github.com/nejci/PRAr.

Opinion-leader mining in social networks is a critical problem in research of the information dissemination process and in public opinion guidance and supervision. Not every social network user has a high probability to be an opinion leader. However, most mining methods identify opinion leaders among users in the whole network, which adds unnecessary calculations. To solve this problem, we propose a rank after clustering (RaC) algorithm to mine opinion leaders in social networks with a phased-clustering perspective, which has the following aspects: (1) Aiming to reduce the scale of calculation, the clustering stage clusters users in social networks using a K-means algorithm according to topological information to find the set of opinion leader candidates; (2) The ranking stage determines the user ranks of opinion leader candidates by both their activeness and influence, and we accumulate the followers' influence weighted by degree of attention when assessing user influence. In experiments, a new indicator, the C-value, and simulations based on the linear threshold model are used to evaluate the performance of the RaC algorithm. The results show that RaC is effective and accurate.

Density peaks clustering (DPC) is a density-based clustering algorithm with excellent clustering performance including accuracy, automatically detecting the number of clusters, and identifying center points. However, DPC has some shortcomings to be addressed before it can be widely applied. For example, sensitive predefined parameter is not suitable for manifold datasets, and decision graph easily causes the wrong center points. To address these issues, a new DPC algorithm based on weighted k-nearest neighbors and geodesic distance (DPC-WKNN-GD) is proposed in this article to improve the clustering performance for manifold and non-manifold datasets. The DPC-WKNN-GD introduces the weighted k-nearest neighbors based on Euclidean distance to optimize the local density $\rho $ . Inspired by DPC-GD, the DPC-WKNN-GD redefines the distance $\delta $ based on geodesic distance. The experimental results on artificial and real-world datasets, including image datasets, show that the DPC-WKNN-GD outperforms the state-of-the-art comparison algorithms on both manifold and non-manifold datasets. In addition, the DPC-WKNN-GD completely overcomes the DPC-GD defect, in which the decision graph displays misleading center points.

Accurate solar generation prediction is of great significance for grid dispatching and operation of photovoltaic power plants. In this paper, we propose a novel solar generation forecasting method based on cluster analysis and ensemble model. Two common ways to improve prediction accuracy are adopted. We first conduct cluster analysis based on solar generation to obtain a weather regime, which improves the computational efficiency and avoids the difficulty in selecting weather variables to participate in the clustering process. Then random forests with different parameters is established for different weather regimes, which is used as component models in the followed ensemble model. Finally, we weighted the predictions from different weather regimes to get the final results. To avoid manual design weights, ridge regression is used to calculate weights for each weather regime automatically. A large number of experiments have been carried out on freely available data sets to verify the performance of the proposed method. The experimental results show that our method predicts solar generation more accurately, which has broad prospects in practical application.

Unemployment in Spain is one of the biggest concerns of its inhabitants. Its unemployment rate is the second highest in the European Union, and in the second quarter of 2018 there is a 15.2% unemployment rate, some 3.4 million unemployed. Construction is one of the activity sectors that have suffered the most from the economic crisis. In addition, the economic crisis affected in different ways to the labour market in terms of occupation level or location. The aim of this paper is to discover how the labour market is organised taking into account the jobs that workers get during two periods: 2011-2013, which corresponds to the economic crisis period, and 2014-2016, which was a period of economic recovery. The data used are official records of the Spanish administration corresponding to 1.9 and 2.4 million job placements, respectively. The labour market was analysed by applying unsupervised machine learning techniques to obtain a clear and structured information on the employment generation process and the underlying labour mobility. We have applied two clustering methods with two different technologies, and the results indicate that there were some movements in the Spanish labour market which have changed the physiognomy of some of the jobs. The analysis reveals the changes in the labour market: the crisis forces greater geographical mobility and favours the subsequent emergence of new job sources. Nevertheless, there still exist some clusters that remain stable despite the crisis. We may conclude that we have achieved a characterisation of some important groups of workers in Spain. The methodology used, being supported by Big Data techniques, would serve to analyse any alternative job market.

Topic extraction is an essential task in bibliometric data analysis, data mining and knowledge discovery, which seeks to identify significant topics from text collections. The conventional topic extraction schemes require human intervention and involve also comprehensive pre-processing tasks to represent text collections in an appropriate way. In this paper, we present a two-stage framework for topic extraction from scientific literature. The presented scheme employs a two-staged procedure, where word embedding schemes have been utilized in conjunction with cluster analysis. To extract significant topics from text collections, we propose an improved word embedding scheme, which incorporates word vectors obtained by word2vec, POS2vec, word-position2vec and LDA2vec schemes. In the clustering phase, an improved clustering ensemble framework, which incorporates conventional clustering methods (i.e., k-means, k-modes, k-means++, self-organizing maps and DIANA algorithm) by means of the iterative voting consensus, has been presented. In the empirical analysis, we analyze a corpus containing 160,424 abstracts of articles from various disciplines, including agricultural engineering, economics, engineering and computer science. In the experimental analysis, performance of the proposed scheme has been compared to conventional baseline clustering methods (such as, k-means, k-modes, and k-means++), LDA-based topic modelling and conventional word embedding schemes. The empirical analysis reveals that ensemble word embedding scheme yields better predictive performance compared to the baseline word vectors for topic extraction. Ensemble clustering framework outperforms the baseline clustering methods. The results obtained by the proposed framework show an improvement in Jaccard coefficient, Folkes & Mallows measure and F1 score.

Human activity recognition (HAR) is a hot research topic which aims to understand human behavior and can be applied in various applications. However, transitions between activities are usually disregarded due to their low incidence and short duration when compared against other activities, while in fact, transitions can affect the performance of the recognition system if not dealt with properly. In this paper, we propose and implement a systematic human activity recognition method to recognize basic activities (BA) and transitional activities (TA) in a continuous sensor data stream. First, raw sensor data are segmented into fragments with sliding window and the features are constructed based on window segmentation. Then, cluster analysis with K-Means is used to aggregate activity fragments into periods. Next, generally, realize the classification of BA and TA according to the shortest duration of the BA, and then deal with the hidden phenomenon of BA. Third, the fragments between adjacent BA are evaluated to decide whether they are TA or disturbance process. Finally, random forest classifier is used to accurately recognize BA and TA. The proposed method is evaluated on the public dataset SBHAR. The results demonstrate that our method effectively recognizes different activities and can deliver high accuracy with all activities considered.

Abnormal behavior analysis in crowded scenes is a challenging task in the computer vision field. Determining how to quickly and accurately locate abnormal behavior in a crowd, especially in airports, shopping malls, stations, and other practical application scenarios, has become the key to the timely handling of mass incidents. The authors have developed an embedded video analysis robot capable of local processing for crowd anomaly detection in application scenarios. In recent years, many methods of crowd anomaly detection have been proposed by scholars, but the performance of these methods when applied to video service robots is unsatisfactory. In this paper, an anomaly analysis method based on improved k-means is proposed. This method combines mean shift and the k-means classification method to achieve rapid and accurate crowd anomaly detection. A quantitative experimental evaluation was carried out on a video service robot, and the results demonstrate that the method is effective for the detection of abnormal behavior on multiple and publicly available video sequences.

Clustering is a classic combined optimization problem that is widely used in pattern recognition, image processing, market analysis and so on. However, the efficiency of clustering algorithms decreases as the amount of data increases. In addition, most of the existing methods optimize only one objective and therefore may be suitable only for datasets with certain features. To address these limitations, in this paper, we develop a new hybrid chain-hypergraph P system (named HCHPS), which makes full use of the parallelism of P systems as well as the advantages of chain and hypergraph topology structures for accurate and efficient clustering. Our new P system comprises three types of subsystems, i.e., reaction chain membrane subsystems, local communication membrane subsystems and global ensemble membrane subsystems. Each type of subsystems is implemented end-to-end in HCHPS with new rules and membrane structures in parallel. In particular, to obtain efficient clustering center objects and make the algorithm robust to data with various features, the reaction chain membrane subsystems perform three different multiobjective strategies simultaneously by new chain evolution rules. To increase the population diversity of cluster centers, the local communication membrane subsystems utilize transport rules between membranes for coevolution of nondominated objects. The global ensemble membrane subsystems conduct a new dense representation multisize ensemble strategy to further improve the accuracy of the final results. Evaluations on two artificial data sets and 17 real-life data sets demonstrate the robustness of the proposed method in correctly clustering data sets with different dimensions and shapes. Our experimental results outperform those of both baseline and state-of-the-art methods. Moreover, benefiting from the parallelism, HCHPS is less time consuming than other methods, featuring an average completion time of 28.07 seconds on the 17 real-life data sets. Moreover, an ablation study shows that our proposed components are critical for effective cluster analysis.

Predicting the next-day power demand has been one of the most important research areas in the electricity industry for the past decade. A successful and more accurate prediction can help both the policy-makers and consumers to plan their bidding strategies. Self-organizing maps (SOM) and K-means are the two classical algorithms among many clustering ones. In this paper, they were used to predict the electric demand with the combination of the least-square support vector machine (LSSVM). The purpose of this paper is to analyze the capacity of the SOM-LSSVM by comparing it with the K-means-LSSVM and a single LSSVM for one-day-ahead electric demand forecasting. A new parallel-grid search algorithm for LSSVM is also proposed to improve orecasting speed. The empirical testing shows that all of the three models provide good prediction results, but the results of the mean absolute percentage error obtained with the SOM-LSSVM method can achieve better prediction accuracy compared with the K-means-LSSVM and a single LSSVM models.

Density peaks clustering (DPC) is a density-based clustering algorithm with excellent clustering performance including accuracy, automatically detecting the number of clusters, and identifying center points. However, the local density of DPC strongly depends on the cutoff distance which must be prespecified; in addition, the strategy assigns each remaining point to the same cluster as its nearest neighbor of higher density in descending order of local density, which is likely to cause cluster label error propagation. To overcome these limitations, we propose an improved DPC by introducing weighted local density sequence and two-stage assignment strategies, called DPCSA. Many previous improved DPC algorithms neglect additional complexity, whereas DPCSA incorporates the nearest neighbor dynamic table to enhance clustering efficiency. The experimental results for 12 artificial and 11 real-world datasets, including Olivetti face, verify that the DPCSA clustering performance is significantly superior to DPC and DPC via heat diffusion (HDDPC), and slightly superior to fuzzy weighted k-nearest neighbors density peak clustering (FKNNDPC). In addition, the DPCSA is more computationally efficient than FKNNDPC and HDDPC, but less than DPC. The source code of DPCSA is available at https://www.github.com/Yu123456/DPCSA.

In order to promote the efficient development of agriculture, the Internet of Things technology is applied to modern agricultural production, and a smart agricultural system is constructed in this paper. Moreover, the data visualization analysis and cluster analysis are used to find the key technologies in the development of the intelligent agriculture, which can effectively improve the production efficiency and ensure the quality of the agricultural products. Intellectualized products are gradually integrated into agricultural production, and the development of the Internet of Things also gives technical impetus to intelligent agriculture. Through the functions of sensing, identification, transmission, monitoring, and feedback of the Internet of Things, related agricultural activities can be accurately completed, which not only saves the time cost of farmers but also improves the crop yields and, ultimately, benefits the farmers. Therefore, the Internet of Things is used for agriculture induction, identification, monitoring, and feedback, and it is also applied to find the key technology in the application process to achieve intelligent, scientific, and efficient agriculture. It also has certain reference significance for the research of the front-end induction recognition and the intelligence of the Internet of Things in the aquaculture industry.

Internet of Things technology has brought new opportunities for rural informatization. The quality of rural information environment is directly related to the level of rural informatization. So accurately understanding the status and level of information environment in rural areas is a prerequisite for rural informatization. This paper assesses differences in rural information environment by developing a framework based on a fuzzy comprehensive evaluation method to measure information environment changes. The assessment focuses on three aspects: one is the evaluation of the overall rural information environment based on macro-factors in time dimension, the other is in 31 different provinces in space dimension, and the third is the evaluation of regional rural information environment based on macro-and subjective factors. The Probit model reveals the influence of various factors on the rural information environment, which constitutes the primary and secondary indicators of the evaluation system. The fuzzy comprehensive evaluation method is adopted to evaluate the rural information environment, and the cluster analysis is applied to the results. The results show that the differences in rural information environment exist objectively, which are the result of a variety of subjective and objective factors. In addition, the differences in rural information environment promote the diversified development path of rural information construction. These findings have clear policy implication for the rural ministry, since understanding the changes and differences in the rural information environment is important for their successful development. Results of the experimental applications of this evaluation system are given to illustrate the proposed method.

Permeable concrete is widely used as a road surfacing material due to its sturdiness and ability to be quickly repaired. The porosity of concrete has been used as a predictive indicator for the properties of the concrete. Traditional methods for measuring this porosity are feasible but can be time-consuming. In this paper, we propose a particle swarm optimization-based projection algorithm for visualization of the high-dimensional data as a 2-D scatter plot for detecting porosity in permeable concrete from X-ray computerized tomography images. We regard the proposed projection algorithm as an improved version of Sammon's nonlinear mapping. The projected scatter plot allows for a straightforward analysis of the inherent structure of clusters within scanned images. Several data sets, including artificial data sets and real-life imaging data, were tested to demonstrate the performance of the proposed projection algorithm. The model created in this paper can augment the traditional methods for examining porosity by providing visual images for decision makers to make correct decisions for future problems. With an accuracy of >99%, the visualized images provide a clearer understanding of the inner structure of pervious concrete and enhance the study of the correlation between the properties of the concrete.

Clustering as an unsupervised learning technique has been widely used in practice. In this paper, a novel clustering algorithm based on region segmentation (CRS) is proposed. It aims to automatically evolve the optimal number of clusters as well as the clusters of the data sets based on the data density. First, a new data density is given based on the reverse near neighbor enhancement which can make the clusters detection more effectively. Then, the multiple sub-region centers can be determined through the data density. Moreover, a merge criterion is proposed to make the relevant regions be merged and obtain the final clustering results. The proposed algorithm does not need to know the number of clusters in advance and no threshold limit. Therefore, it can be used more widely. In the experiments, we compare the performance of our CRS algorithm with DBSCAN, IS-DBSCAN, STClu, DP, and SCDOT algorithms on synthetic, and real-world data sets. Experimental results demonstrated that the NMI, ACC, F1 and ARI obtained by CRS algorithm is always better than that obtained by the other algorithms for the same data sets.