Search

In this paper, a new data-driven autonomous fuzzy clustering (AFC) algorithm is proposed for static data clustering. Employing a Gaussian-type membership function, AFC firstly uses all the data samples as micro-cluster medoids to assign memberships to each other and obtains the membership matrix. Based on this, AFC chooses these data samples that represent local models of data distribution as cluster medoids for initial partition. It then continues to optimize the cluster medoids iteratively to obtain a locally optimal partition as the algorithm output. Moreover, an online extension is introduced to AFC enabling the algorithm to cluster streaming data chunk-by-chunk in a one pass manner. Numerical examples based on a variety of benchmark problems demonstrate the efficacy of the AFC algorithm in both offline and online application scenarios, proving the effectiveness and validity of the proposed concept and general principles.

We present new algorithms to classify structural ensembles of macromolecules, based on the recently proposed extended similarity measures. Molecular Dynamics provides a wealth of structural information on systems of biologically interest. As computer power increases we capture larger ensembles and larger conformational transitions between states. Typically, structural clustering provides the statistical mechanics treatment of the system to identify relevant biological states. The key advantage of our approach is that the newly introduced extended similiarity indices reduce the computational complexity of assessing the similarity of a set of structures from O(N2) to O(N). Here we take advantage of this favorable cost to develop several highly efficient techniques, including a linear-scaling algorithm to determine the medoid of a set (which we effectively use to select the most representative structure of a cluster). Moreover, we use our extended similarity indices as a linkage criterion in a novel hierarchical agglomerative clustering algorithm. We apply these new metrics to analyze the ensembles of several systems of biological interest such as folding and binding of macromolecules (peptide,protein,DNA -protein). In particular, we design a new workflow that is capable of identifying the most important conformations contributing to the protein folding process. We show excellent performance in the resulting clusters (surpassing traditional linkage criteria), along with faster performance and an efficient cost-function to identify when to merge clusters.

The knowledge of the climate pattern for a particular region is important to alleviate the impact of climate change and protect the environment by taking appropriate actions based on geographical knowledge. It is also equally important for water resources planning and management. In this study, the regional disparities and similarities have been revealed among different climate stations or regions in Bangladesh based on different climatological factors such as rainfall, temperatures, relative humidity, sea level pressure, cloud cover, wind speed, the sunshine hour. We have selected one of the best-fitted algorithms for particular climate data from three multivariate clustering approaches named hierarchical clustering, partitioning around medoids (PAM), and K-means clustering by using different validation tests. Four homogeneity tests (Mann-Kendall Test, Pettitt's test, Buishand Range Test, Standard Normal Homogeneity Test) also have been performed for each of the clusters created based on several factors. The results suggest that the climate regions or meteorological stations of Bangladesh can be clustered into two groups based on a combination of climatological variables. According to the findings, there is a huge variation between the two groups in terms of climatological factors. The first group (cluster 1) is located in the northern part of the country that includes drought-prone and vulnerable regions, whereas, the second group (cluster 2) contains rain-prone and hilly regions, which are mostly situated in the southern part. All newly defined clusters show homogeneous behavior with few exceptions such as clusters based on sea level pressure are not homogeneous.

The latent demand to optimize costs and customer service has been fostered in the current economic situations, characterized by high competitiveness and disruption in supply chains, placing inventories as a vital sector with significant potential to implement improvements in firms. Inventory management that is done correctly has a favorable impact on logistics performance indexes. Warehousing operations account for around 15% of logistics expenditures in terms of dollars. This article employs a method based on the Partitioning Around Medoids algorithm that incorporates, in a novel way, the application of a strategy for locating the optimal picking point based on cluster classification, taking into account the qualitative and quantitative factors that have the greatest impact or priority on inventory management in the company. The results obtained with this model improve the routes of distributed materials based on the identification of their characteristics such as the frequency of collection and handling of materials, allowing for the reorganization and expansion of storage capacity of the various SKUs, moving from a classification by families to a cluster classification. This article shows a suggestion for a warehouse distribution design using data mining techniques, which uses indicators and key qualities for operational success for a case study in a corporation, as well as an approach to improve inventory management decision-making.

The paper is dedicated to the development and comparative experimental analysis of semi-supervised learning approaches based on a mix of unsupervised and supervised approaches for the classification of datasets with a small amount of labeled data, namely, identifying to which of a set of categories a new observation belongs using a training set of data containing observations whose category membership is known. Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Unlabeled data, when used in combination with a small quantity of labeled data, can produce significant improvement in learning accuracy.The goal is semi-supervised methods development and analysis along with comparing their accuracy and robustness on different synthetics datasets. The proposed approach is based on the unsupervised K-medoids methods, also known as the Partitioning Around Medoid algorithm, however, unlike K-medoids the proposed algorithm first calculates medoids using only labeled data and next process unlabeled classes – assign labels of nearest medoid. Another proposed approach is the mix of the supervised method of K-nearest neighbor and unsupervised K-Means. Thus, the proposed learning algorithm uses information about both the nearest points and classes centers of mass. The methods have been implemented using Python programming language and experimentally investigated for solving classification problems using datasets with different distribution and spatial characteristics. Datasets were generated using the scikit-learn library. Was compared the developed approaches to find average accuracy on all these datasets. It was shown, that even small amounts of labeled data allow us to use semi-supervised learning, and proposed modifications ensure to improve accuracy and algorithm performance, which was demonstrated during experiments. And with the increase of available label information accuracy of the algorithms grows up. Thus, the developed algorithms are using a distance metric that considers available label information. Дана робота присвячена розробці та порівняльному аналізу алгоритмів навчання з частковим залученням вчителя, заснованих на поєднанні неконтрольованих та контрольованих підходів до класифікації наборів даних з невеликою кількістю маркованих даних, а саме виявленню, до якої з набору категорій нове спостереження належить за допомогою навчального набору даних, що містить спостереження, приналежність до категорії яких відома. Навчання з частковим залученням вчителя – це підхід до машинного навчання, який поєднує невелику кількість маркованих даних з великою кількістю немаркованих даних під час навчання. Немарковані дані, якщо їх використовувати в поєднанні з невеликою кількістю маркованих даних, можуть значно покращити точність навчання. Метою роботи є розробка та аналіз методів навчання з частковим залученням вчителя, а також порівняння їх точності та надійності на різних наборах штучних даних. Запропонований підхід заснований на методі неконтрольованого навчання K-медоїдів, також відомий як алгоритм Розбиття навколо медоїдів, однак, на відміну від K-медоїдів, запропонований алгоритм спочатку обчислює медоїди, використовуючи лише маркованi дані, а далі обробляє не марковані елементи - призначає мітки найближчих медоїд. Іншим запропонованим підходом є поєднання контрольованого методу K-найближчіх сусідів та неконтрольованого K-середніх. При цьому запропонований алгоритм навчання використовує інформацію як про найближчі точки, так і про класи центрів маси. Методи були реалізовані з використанням мови програмування Python та експериментально досліджені для вирішення проблем класифікації з використанням наборів даних з різними розподілом та просторовими характеристиками. Набори даних були сформовані за допомогою бібліотеки scikit-learn. Було порівняно розроблені підходи за їх середню точність за всіма датасетами. Було показано, що навіть невеликі кількості маркованих даних дозволяють використовувати навчання з частковим залученням вчителя, а запропоновані модифікації забезпечують підвищення точності та роботи алгоритму, що було продемонстровано під час експериментів. І зі збільшенням доступної інформації про ярлики, точність алгоритмів зростає. Таким чином розроблені алгоритми використовують метрику відстані, яка враховує доступну інформацію про ярлик.

Herein, a computational platform FluorSimStudio was developed for processing fluorescence decay curves in molecular systems, which implements the concept of complex analysis of experimental information based on the simulation modeling and data mining methods. Data analysis includes partitioning the fluorescence decay curves into clusters according to the degree of likeness in some measure of similarity, finding the median cluster members (medoids), applying the data reduction method and visualizing the experimental data in a two-dimensional space. Analysis of the decay curves is carried out by the analytical or simulation models of optical processes occurring in molecular systems. The visualization of data clusters in the original and transformed time spaces is done with the aim of user interaction. A functional scheme of the platform is proposed, the choice of software for ensuring high computing performance is substantiated, a web application of the platform is implemented ( https://dsa-cm.shinyapps.io/FluorSimStudio ), and the results of a comparative analysis of the simulation algorithms are presented. The performance of the computational platform was confirmed by examples of the analysis of data sets representing systems of free fluorophores and in the presence of the Forster electronic excitation energy transfer process. The computational platform is an open system and allows permanent addition of complex analysis models, taking into account the development of new algorithms for modeling the energy transfer processes in molecular systems, studied with the use of time-resolved fluorescence spectroscopy systems.

CNNs, RNNs, GCNs, and CapsNets have shown significant insights in representation learning and are widely used in various text mining tasks such as large-scale multi-label text classification. Most existing deep models for multi-label text classification consider either the non-consecutive and long-distance semantics or the sequential semantics. However, how to coherently take them into account is still far from studied. In addition, most existing methods treat output labels as independent medoids, ignoring the hierarchical relationships among them, which leads to a substantial loss of useful semantic information. In this paper, we propose a novel hierarchical taxonomy-aware and attentional graph capsule recurrent CNNs framework for large-scale multi-label text classification. Specifically, we first propose to model each document as a word order preserved graph-of-words and normalize it as a corresponding word matrix representation preserving both the non-consecutive, long-distance and local sequential semantics. Then the word matrix is input to the proposed attentional graph capsule recurrent CNNs for effectively learning the semantic features. To leverage the hierarchical relations among the class labels, we propose a hierarchical taxonomy embedding method to learn their representations, and define a novel weighted margin loss by incorporating the label representation similarity. Extensive evaluations on three datasets show that our model significantly improves the performance of large-scale multi-label text classification by comparing with state-of-the-art approaches.

Objective. The progressive demographic increase of the ageing population, with a consequent increase in the average age, is generating important growth in the demand for healthcare services due to the revolving- door and over-crowding phenomena. The aim of this study is to identify the degree of the frailty of the elderly population and the need to activate periodical home assistance in order to counter the aforementioned phenomena. Methods. A pilot study for adaptation of the SvaMa scale. Results. The study recruited an overall sample of n = 80, aged between 65 and 100 years old. Results were analyzed using descriptive statistical analysis, and the Spearman rank correlation coefficient highlights a strong correlation between the different sections of this scale. The data were also analyzed using the Partitioning Around Medoids method, which found that the sample had a good cognitive state but presented functional and mobility issues.Conclusions. From the data that emerged, we can affirm that the modified S.Va.M.A. scale represents a suitable, fluid, simple instrument to be used whenever a multidimensional assessment of elderly people is necessary.

A VANET or vehicular Ad Hoc Network is known for its fast topology transition and node mobility, contributing to its attributes as an ad hoc network. The aspect of gathering the nodes, making this system extremely vigorous is known as clustering. However, in certain cases, it is not possible to keep track of the nodes which will results in network issues due to energy insufficiency during execution. Hence this will lead to primary energy management problems faced during the routing protocol which take into consideration the node lifetime. To address this discrepancy, we have proposed a novel optimization technique based on clustering. It has been observed that the proposed methodology will further improve the effectiveness of V2V communication. In this paper, clustering of the vehicle nodes is done using K-Medoid clustering model and are then used to improve energy efficiency. A metaheuristic algorithm is used to establish an energy efficient communication methodology. Based on the simulation analysis performed, it is seen that this methodology requires lesser execution time and improves the nodes’ energy efficiency.

Context. The analytical procedures used in the audit are currently based on data mining techniques. The work solves the problem of increasing the efficiency and effectiveness of analytical audit procedures by clustering based on spectral decomposition. The object of the research is the process of auditing the compliance of payment and supply sequences for raw materials. Objective. The aim of the work is to increase the effectiveness and efficiency of the audit due to the method of spectral clustering of sequences of payment and supply of raw materials while automating procedures for checking their compliance. Method. The vectors of features are generated for the objects of the sequences of payment and supply of raw materials, which are then used in the proposed method. The created method improves the traditional spectral clustering method by automatically determining the number of clusters based on the explained and sample variance rule; automatic determination of the scale parameter based on local scaling (the rule of K-nearest neighbors is used); resistance to noise and random outliers by replacing the k-means method with a modified PAM method, i.e. replacing centroid clustering with medoid clustering. As in the traditional approach, the data can be sparse, and the clusters can have different shapes and sizes. The characteristics of evaluating the quality of spectral clustering are selected. Results. The proposed spectral clustering method was implemented in the MATLAB package. The results obtained made it possible to study the dependence of the parameter values on the quality of clustering. Conclusions. The experiments carried out have confirmed the efficiency of the proposed method and allow us to recommend it for practical use in solving audit problems. Prospects for further research may lie in the creation of intelligent parallel and distributed computer systems for general and special purposes, which use the proposed method for segmentation, machine learning and pattern recognition tasks.

Data analytics methods have been increasingly applied to understanding materials chemistry, processing due to the manufacturing approach, and uni-axial and cyclic property relationships in the highly complex space of alloy design. There are several benefits to applying data analytics to this space, including the ability to manage non-linearities in the responses of the alloy attributes and the resulting mechanical properties. However, key difficulties in applying and understanding the results of data analytics include the often lack of reported assumptions and data processing steps necessary to improve interpretation and reproducibility in derived results. In this work, the methods used to generate clustering and correlation analyses for experimental 9% Cr ferritic-martensitic steel data were investigated and the resulting implications for mechanical property predictions were assessed. This work uses principal component analysis, partitioning around medoids, t-SNE, and k-means clustering to investigate trends in composition, processing and microstructure information with creep and tensile properties, building on work done previously using a smaller version of the same dataset. The initial assumptions, preprocessing steps and methods are investigated and outlined in order to depict the fine level of detail required to convey the steps taken to process data and produce analytical results. The variations in the resulting analyses are explored due to the influence of new and more varied data.

In wireless sensor networks, sensor nodes, the miniature embedded devices, have limitation of energy, storage, computing, and etc. One of the tasks of the nodes is to use their limited resources to complete work efficiently. Choosing high quality link communication can effectively save energy. In this paper, we propose a link quality estimation model that is based on deep forest. To avoid a noise sample becoming a center point in the clustering, we use an improved K-medoids algorithm based on step increasing and optimizing medoids (INCK) when dividing the link quality grades. During the sample preprocessing stage, the Pauta criterion is used to delete the noise link samples, and we fill the mean value of each grade into the missing values. The feature extraction performance of deep forest is improved by combining the stratified sampling to change the unbalance distribution of link quality samples. And then the Stratified Sampling Cascade Forest link quality estimation (SCForest-LQE) is constructed by combining stratified sampling with cascade forest. The experiments are conducted in three real application scenarios. Compared with the existing six link quality estimation models, SCForest-LQE has better estimation performance and stability.

In this research, k-medoid clustering is modelled and evaluated for the capacitated vehicle routing problem (CVRP). The k-medoid clustering method creates petal-shaped clusters, which could be an effective method to create routes in the CVRP. To determine routes from the clusters, an existing metaheuristic — the ruin and recreate (R&R) method — is applied to each generated cluster. The results are benchmarked to those of a well-known clustering method, k-means clustering. The performance of the methods is measured in terms of travel cost and distance travelled, which are well-known metrics for the CVRP. The results show that k-medoid clustering method outperforms the benchmark method for most instances of the test datasets, although the CVRP without any predefined clusters still provides solutions that are closer to optimal. Clustering remains a reliable distribution management tool and reduces the processing requirements of large-scale CVRPs.

Earthquake is the shaking of the earth's surface due to the shift in the earth's plates. This disaster often happens in Indonesia due to the location of the country on the three largest plates in the world and nine small others which meet at an area to form a complex plate arrangement. An earthquake has several impacts which depend on the magnitude and depth. This research was, therefore, conducted to classify earthquake data in Indonesia based on the magnitudes and depths using one of the data mining techniques which is known as clustering through the application of k-medoids and k-means algorithms. However, k-medoids group data into clusters with medoid as the centroid and it involves using clustering large application (CLARA) algorithm while k-means divide data into k clusters where each object belongs to the cluster with the closest average. The results showed the best clustering for earthquake data in Indonesia based on magnitude and depth is the CLARA algorithm and five clusters were found to have total members of 2231, 1359, 914, 2392, and 199 objects for cluster 1 to cluster 5 respectively.

Innovations on a talk show on television can be a threat. Audience will be divided into groups so that it can make a downgrade rating program. Program ratings affect companies that will use advertising services. Television companies will go bankrupt. The biggest source of income is sales of advertising services. One way to overcome them can be analyzed in public opinion. The results of the analysis can provide information about the attractiveness of the community towards the program. But the analysis process takes a long time and can be done only by a competent person so another process is needed to get the results of the analysis that is fast and can be done by anyone. In this study using K-Medoids Clustering in the process of identifying public opinion. The clustering process known as unsupervised learning will be combined with the labeling process. The previous episode's tweet data will be labeled and then used to obtain the predicted labels from other cluster members. Before going through the clustering stage, the tweet data will go through the text preprocessing stage then transformed into a numeric form based on the appearance of the word. Transformation data will be clustered by calculating proximity using Cosine Similarity. Labels from the Medoids cluster will be used on unlabeled tweet data. The cluster results were tested using the Silhouette Coefficient method to get 0.19 results. However, this method successfully predicted public opinion and achieved an accuracy of 80%.

Coupled human and natural systems (CHANS) are frequently represented by large datasets with varied data including continuous, ordinal, and categorical variables. Conventional multivariate analyses cannot handle these mixed data types. In this paper, our goal was to show how a clustering method that has not before been applied to understanding the human dimension of CHANS: a Gower dissimilarity matrix with partitioning around medoids (PAM) can be used to treat mixed-type human datasets. A case study of land managers responsible for invasive plant control projects across rivers of the southwestern U.S. was used to characterize managers’ backgrounds and decisions, and project properties through clustering. Results showed that managers could be classified as “federal multitaskers” or as “educated specialists”. Decisions were characterized by being either “quick and active” or “thorough and careful”. Project goals were either comprehensive with ecological goals or more limited in scope. This study shows that clustering with Gower and PAM can simplify the complex human dimension of this system, demonstrating the utility of this approach for systems frequently composed of mixed-type data such as CHANS. This clustering approach can be used to direct scientific recommendations towards homogeneous groups of managers and project types.

The process of K-medoids algorithm is that it first selects data randomly as initial centers to form initial clusters. Then, based on PAM (partitioning around medoids) algorithm, centers will be sequential replaced by all the remaining data to find a result has the best inherent convergence. Since PAM algorithm is an iterative ergodic strategy, when the data size or the number of clusters are huge, its expensive computational overhead will hinder its feasibility. The authors use the fixed-point iteration to search the optimal clustering centers and build a FPK-medoids (fixed point-based K-medoids) algorithm. By constructing fixed point equations for each cluster, the problem of searching optimal centers is converted into the solving of equation set in parallel. The experiment is carried on six standard datasets, and the result shows that the clustering efficiency of proposed algorithm is significantly improved compared with the conventional algorithm. In addition, the clustering quality will be markedly enhanced in handling problems with large-scale datasets or a large number of clusters.

The increasing availability of sensors imaging cloud and precipitation particles, like the Multi-Angle Snowflake Camera (MASC), has resulted in datasets comprising millions of images of falling snowflakes. Automated classification is required for effective analysis of such large datasets. While supervised classification methods have been developed for this purpose in the recent years, their ability to generalize is limited by the representativeness of their labeled training datasets, which are affected by the subjective judgment of the expert and require significant manual effort to derive. An alternative is unsupervised classification, which seeks to divide a dataset into distinct classes without expert-provided labels. In this study, we introduce an unsupervised classification scheme based on a generative adversarial network (GAN) that learns to extract the key features from the images. Each image is then associated with a distribution of points in the feature space, and these distributions are used as the basis of K-medoids classification and hierarchical clustering. We find that the classification scheme is able to separate the dataset into distinct classes, each characterized by a particular size, shape and texture of the snowflake image, providing signatures of the microphysical properties of the snowflakes. This finding is supported by a comparison of the results to an existing supervised scheme. Although training the GAN is computationally intensive, the classification process proceeds directly from images to classes with minimal human intervention and therefore can be repeated for other MASC datasets with minor manual effort. As the algorithm is not specific to snowflakes, we also expect this approach to be relevant to other applications.

K-medoids clustering is categorized as partitional clustering. K-medoids offers better result when dealing with outliers and arbitrary distance metric also in the situation when the mean or median does not exist within data. However, k-medoids suffers a high computational complexity. Partitioning Around Medoids (PAM) has been developed to improve k-medoids clustering, consists of build and swap steps and uses the entire dataset to find the best potential medoids. Thus, PAM produces better medoids than other algorithms. This research proposes the parallelization of PAM in k-medoids clustering on GPU to reduce computational time at the swap step of PAM. The parallelization scheme utilizes shared memory, reduction algorithm, and optimization of the thread block configuration to maximize the occupancy. Based on the experiment result, the proposed parallelized PAM k-medoids is faster than CPU and Matlab implementation and efficient for large dataset.

A method is proposed for processing large data sets of fluorescence decay kinetic curves using data mining algorithms to determine the parameters of biophysical and optical processes in molecular systems. The idea of this method involves breaking the initial set of fluorescence decay curves into clusters in terms of some degree of similarity, finding medoids of the clusters, applying a dimensionality reduction method to the data and imaging the experimental data in two- and three-dimensional space, and analyzing the decay curves of the medoids using analytic or simulation models. The applicability of the method is examined for the example of analyzing sets of data representing systems of fluorophores. This method requires substantially less time and calculations of the analytic approximation functions, while the accuracy of the estimated parameters is higher than in the classical approach.

The k-nearest-neighbor (kNN) decision rule is a simple and robust classifier for text categorization. The performance of kNN decision rule depends heavily upon the value of the neighborhood parameter k. The method categorize a test document even if the difference between the number of members of two competing categories is one. Hence, choice of k is crucial as different values of k can change the result of text categorization. Moreover, text categorization is a challenging task as the text data are generally sparse and high dimensional. Note that, assigning a document to a predefined category for an arbitrary value of k may not be accurate when there is no bound on the margin of majority voting. A method is thus proposed in spirit of the nearest-neighbor decision rule using a medoid-based weighting scheme to deal with these issues. The method puts more weightage on the training documents that are not only lie close to the test document but also lie close to the medoid of its corresponding category in decision making, unlike the standard nearest-neighbor algorithms that stress on the documents that are just close to the test document. The aim of the proposed classifier is to enrich the quality of decision making. The empirical results show that the proposed method performs better than different standard nearest-neighbor decision rules and support vector machine classifier using various well-known text collections in terms of macro- and micro-averaged f-measure.

The wireless sensor networks have become a very hot topic of research in the recent years because of their wide range of applications such as industrial and agricultural monitoring, military surveillance, smart homes etc. These sensors can also be used in locations that are potentially hazardous to the human beings or are just out of our reach at this moment of time. A wireless sensor network is collection of large number of individual sensor nodes. A sensor usually comprises of components for sensing the environment, processing, communicating and a power supply. The major limiting factor of the wireless sensor networks is the limited amount of power that each of the sensors can carry and also the energy in these sensors cannot be replenished easily. So, the major design challenge in a wireless sensor network has always been about reduction in the consumption of energy by the sensors. Clustering is one of the popular methods to reduce energy consumption in wireless sensor networks. Here, we propose a scheme to decrease the energy consumption and prolong the lifetime of wireless sensor network. The main idea behind the scheme is that we try to minimise the communication distance between the individual sensor nodes using the clustering technique.

The aim of the work is to identify a clustering structure for the 20 Italian regions according to the main variables related to COVID-19 pandemic. Data are observed over time, spanning from the last week of February 2020 to the first week of February 2021. Dealing with geographical units observed at several time occasions, the proposed fuzzy clustering model embedded both space and time information. Properly, an Exponential distance-based Fuzzy Partitioning Around Medoids algorithm with spatial penalty term has been proposed to classify the spline representation of the time trajectories. The results show that the heterogeneity among regions along with the spatial contiguity is essential to understand the spread of the pandemic and to design effective policies to mitigate the effects.

[Key message] The strong genetic structure observed in Mediterranean oats affects the predictive ability of genomic prediction as well as the performance of training set optimization methods., [Abstract] In this study, we investigated the efficiency of genomic prediction and training set optimization in a highly structured population of cultivars and landraces of cultivated oat (Avena sativa) from the Mediterranean basin, including white (subsp. sativa) and red (subsp. byzantina) oats, genotyped using genotype-by-sequencing markers and evaluated for agronomic traits in Southern Spain. For most traits, the predictive abilities were moderate to high with little differences between models, except for biomass for which Bayes-B showed a substantial gain compared to other models. The consistency between the structure of the training population and the population to be predicted was key to the predictive ability of genomic predictions. The predictive ability of inter-subspecies predictions was indeed much lower than that of intra-subspecies predictions for all traits. Regarding training set optimization, the linear mixed model optimization criteria (prediction error variance (PEVmean) and coefficient of determination (CDmean)) performed better than the heuristic approach “partitioning around medoids,” even under high population structure. The superiority of CDmean and PEVmean could be explained by their ability to adapt the representation of each genetic group according to those represented in the population to be predicted. These results represent an important step towards the implementation of genomic prediction in oat breeding programs and address important issues faced by the genomic prediction community regarding population structure and training set optimization., This work was supported by the Spanish Ministry of Science and Innovation [PID2019-104518RB-100], (AEI/FEDER, UE) and the regional government through the AGR-253 group and the European Regional and Social Development Funds. LGS is holder of a FPI fellowship from the Spanish Ministry of Economy and Competitiveness [BES-2017-080152]. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 818144, and also the Severo Ochoa Program for Centres of Excellence in R&D. JIS was supported by the Beatriz Galindo Program (BEAGAL18/00115) from the Ministerio de Educación y Formación Profesional of Spain and the Severo Ochoa Program for Centres of Excellence in R&D from the Agencia Estatal de Investigación of Spain, grant SEV-2016-0672 (2017-2021) to the CBGP.

Background: Heterogeneity in Acute Respiratory Distress Syndrome (ARDS), as a consequence of its non-specific definition, has led to a multitude of negative randomised controlled trials (RCTs). Investigators have sought to identify heterogeneity of treatment effect (HTE) in RCTs using clustering algorithms. We evaluated the proficiency of several commonly-used machine-learning algorithms to identify clusters where HTE may be detected. Methods: Five unsupervised: Latent class analysis (LCA), K-means, partition around medoids, hierarchical, and spectral clustering; and four supervised algorithms: model-based recursive partitioning, Causal Forest (CF), and X-learner with Random Forest (XL-RF) and Bayesian Additive Regression Trees were individually applied to three prior ARDS RCTs. Clinical data and research protein biomarkers were used as partitioning variables, with the latter excluded for secondary analyses. For a clustering schema, HTE was evaluated based on the interaction term of treatment group and cluster with day-90 mortality as the dependent variable. Findings: No single algorithm identified clusters with significant HTE in all three trials. LCA, XL-RF, and CF identified HTE most frequently (2/3 RCTs). Important partitioning variables in the unsupervised approaches were consistent across algorithms and RCTs. In supervised models, important partitioning variables varied between algorithms and across RCTs. In algorithms where clusters demonstrated HTE in the same trial, patients frequently interchanged clusters from treatment-benefit to treatment-harm clusters across algorithms. LCA aside, results from all other algorithms were subject to significant alteration in cluster composition and HTE with random seed change. Removing research biomarkers as partitioning variables greatly reduced the chances of detecting HTE across all algorithms. Interpretation: Machine-learning algorithms were inconsistent in their abilities to identify clusters with significant HTE. Protein biomarkers were essential in identifying clusters with HTE. Investigations using machine-learning approaches to identify clusters to seek HTE require cautious interpretation. Funding: NIGMS R35 GM142992 (PS), NHLBI R35 HL140026 (CSC); NIGMS R01 GM123193, Department of Defense W81XWH-21-1-0009, NIA R21 AG068720, NIDA R01 DA051464 (MMC)

Consensus clustering algorithm, which integrates several clustering results obtained by common algorithms, can find a better result that is independent on parameter settings. However, this kind of algorithm is often designed based on simple, such as $K$ -means, algorithms, which is limited by the time complexity. In this work, a P system, a novel branch of bio-inspired computing with inherent parallel and distributed computation, is combined with the consensus clustering algorithm. As a result, an improved consensus clustering algorithm is constructed using the hierarchical membrane structure and parallel evolution mechanism in a cell-like P system with multi-catalysts, where the catalysts are utilized to regulate the parallelism of objects evolution. The integration strategy of the algorithm is based on a revised PAM where only the $q$ -nearest neighbors of the original medoids are considered as candidates for the new medoids. The experimental results indicate that the clustering quality of the proposed algorithm is more robust than well-known consensus clustering algorithms on data sets with noises and outliers. This work gives evidence that the effectiveness and efficiency of consensus clustering algorithms can be improved using P systems.

The increasing usage of the Internet and social networks has produced a significant amount of online textual data. These online textual data led to information overload and redundancy. It is important to eliminate the information redundancy and preserve the time required for reading these online textual data. Thus, there is a persistent need for an automatic text summarization system, which extract the relevant and salient information from a collection of documents, that sharing the same or related topics. Then, presenting this extracted information in a condensed form to preserve the main topics. This paper proposes an automatic, generic, and extractive Arabic multi-document summarization system. The proposed system employs the clustering-based and evolutionary multi-objective optimization methods. The clustering-based method discovers the main topics in the text, while the evolutionary multi-objective optimization method optimizes three objectives based on coverage, diversity/redundancy, and relevancy. The performance of the proposed system is evaluated using TAC 2011 and DUC 2002 datasets. The experimental results are compared using ROUGE evaluation measure. The obtained results showed the effectiveness of the proposed system compared to other peer systems. The proposed system outperformed other peer systems for all ROUGE metrics using TAC 2011. We achieved an F-measure of 38.9%, 17.7%, 35.4%, and 15.8% for Rouge-1, Rouge-2, Rouge-L, and Rouge-SU4, respectively. In addition, the proposed system with DUC 2002 dataset achieved an F-measure of 47.1%, 23.7%, 47.1%, 20.4% for Rouge-1, Rouge-2, Rouge-L, and Rouge-SU4, respectively.

In a centralized cooperative spectrum sensing (CSS) system, it is vulnerable to malicious users (MUs) sending fraudulent sensing data, which can severely degrade the performance of CSS system. To solve this problem, we propose sensing data fusion schemes based on K-medoids and Mean-shift clustering algorithms to resist the MUs sending fraudulent sensing data in this paper. The cognitive users (CUs) send their local energy vector (EVs) to the fusion center which fuses these EVs as an EV with robustness by the proposed data fusion method. Specifically, this method takes a Medoids of all EVs as an initial value and searches for a high-density EV by iteratively as a representative statistical feature which is robust to malicious EVs from MUs. It does not need to distinguish MUs from CUs in the whole CSS process and considers constraints imposed by the CSS system such as the lack of information of PU and the number of MUs. Furthermore, we propose a global decision framework based on fast K-medoids or Mean-shift clustering algorithm, which is unaware of the distributions of primary user (PU) signal and environment noise. It is worth noting that this framework can avoid the derivation of threshold. The simulation results reflect the robustness of our proposed CSS scheme.

El tema de la mediterraneidad ha generado bastante interes en el debate geopolitico, siendo Bolivia uno de los actores principales. Este hecho, junto con las nuevas herramientas de analisis de datos, como la inteligencia artificial y la mineria de datos, motivan el presente este estudio, el cual es pionero dentro de la literatura en el marco del analisis de paises sin salida maritima mediante algoritmos no supervisados de mineria de datos. En este sentido, se estudia y aplica la teoria de formacion de clusters a traves de los algoritmos K-means y PAM (Partitioning Around Medoids) con informacion de indicadores de comercio internacional de 188 paises de un periodo de diez anos, con el proposito de detectar si la condicion de mediterraneidad es un factor limitante en la dinamica comercial de los paises. Los resultados muestran que un subconjunto reducido de los paises mediterraneos, entre ellos Bolivia, habrian aliviado, durante la ultima decada, las restricciones que la mediterraneidad implica en los costos y tiempos de exportacion e importacion.

Globular Clusters (GCs) have historically been subdivided in either two (disk/halo) or three (disk/inner-halo/outer-halo) groups based on their orbital, chemical and internal physical properties. The qualitative nature of this subdivision makes it impossible to determine whether the natural number of groups is actually two, three, or more. In this paper we use cluster analysis on the $(\log M, \log \sigma_0, \log R_e, [Fe/H], \log | Z |)$ space to show that the intrinsic number of GC groups is actually either $k=2$ or $k=3$, with the latter being favored albeit non-significantly. In the $k=2$ case, the Partitioning Around Medoids (PAM) clustering algorithm recovers a metal-poor halo GC group and a metal-rich disk GC group. With $k=3$ the three groups can be interpreted as disk/inner-halo/outer-halo families. For each group we obtain a medoid, i.e. a representative element (NGC $6352$, NGC $5986$, and NGC $5466$ for the disk, inner halo, and outer halo respectively), and a measure of how strongly each GC is associated to its group, the so-called silhouette width. Using the latter, we find a correlation with age for both disk and outer halo GCs where the stronger the association of a GC with the disk (outer halo) group, the younger (older) it is., Comment: 17 figures, MNRAS submitted

Preparation for the modernization of the Kedung Putri Irrigation System (DI Kedung Putri) required a comprehensive assessment of the irrigation pillars, one of which was at the secondary level. To facilitate the assessment and development plan, a clustering was carried out using the k-medoids method, that used a representative data (called medoid) as the cluster center. Then, the decision making was conducted by using the Analytic Hierarchy Process (AHP) method. Performance assessment of 21 secondary channels was stated as the readiness index of irrigation modernization (IKMI). The assessment result showed that 9,52% included in good criteria, 71,43% included in fair criteria, and 19,05% included in poor criteria. Based on these results that DI Kedung Putri was not ready yet to be modernized. For this reason, it was necessary to conduct the system improvement in groups, namely by grouping based on similarities (clustering). The used method was k-medoids clustering using Rapid Miner 9.0 software. The clustering result showed that the optimal cluster number were 4 clusters, with the Davies Bouldin Index (DBI) value -1,959. The members of the 0, 1, 2 and 3 cluster were 6, 6, 8 and 1 secondary channels, respectively. Furthermore, the priority scale in clusters development was needed based on the performance of irrigation pillars on secondary channels. The results of AHP analysis showed that the order of priority development starts from cluster 0, followed by cluster 2, 1, and 3. The recommendations for the development of secondary channels incorporated in cluster, such as increasing water supply, routine infrastructure maintenance, technical assistance, and public campaigns in irrigation management. The secondary channel incorporated in cluster 3 had good performance on all pillars, so it only needed to maintain the existing operation and maintenance patterns., Preparation for the modernization of the Kedung Putri Irrigation System (DI Kedung Putri) required a comprehensive assessment of the irrigation pillars, one of which was at the secondary level. To facilitate the assessment and development plan, a clustering was carried out using the k-medoids method, that used a representative data (called medoid) as the cluster center. Then, decision making was conducted by using the Analytic Hierarchy Process (AHP) method. The performance assessment of 21 secondary channels was stated as the readiness index of irrigation modernization (IKMI). The assessment result showed that 9.52% belonged to good criteria, 71.43% belonged to fair criteria, and 19.05% belonged to poor criteria. Based on these results that DI Kedung Putri was considered not ready yet to be modernized. For this reason, it was necessary to conduct the system improvement in groups, namely by grouping based on similarities (clustering). The used method was k-medoids clustering using Rapid Miner 9.0 software. The clustering result showed that the optimal cluster number was 4clusters, with the Davies Bouldin Index (DBI) value -1.959. The members of the 0, 1, 2 and 3 clusters were 6, 6, 8 and 1 secondary channels, respectively. Furthermore, a priority scale in clusters development was needed based on the performance of irrigation pillars on secondary channels. The results of AHP analysis showed that the order of priority development starts from cluster 0, followed by cluster 2, 1 and 3. The recommendations for the development of secondary channels incorporated in cluster 0, such as increasing water supply, routine infrastructure maintenance, technical assistance, and public campaigns in irrigation management. The secondary channels incorporated in cluster 3 had good performance on all pillars, so it only needed to maintain the existingoperation and maintenance patterns.

A nondestructive multiparametric protocol for chert investigations (NM-PCI) was developed to provide objective criteria for raw material procurements and provenance investigations of archaeological cherts. For this purpose, macroscopic and mesoscopic characteristics (structures, textures and paleontological contents) were used to describe the main visible chert features as well as colourimetric and geochemical data acquired with portable devices. Data handling was performed by means of cluster analysis multivariate statistics, with an appropriate selection of algorithms to process and visualize mixed data matrices (partitioning around medoids (PAM), t-distributed stochastic neighbor embedding (t-SNE)). The protocol was applied to chert samples from the Gargano Promontory (Italy, Apulia) mining complex located in the districts of Peschici, Vieste and Mattinata. These mines, exploited for approximately four millennia, constitute an exceptional prehistoric record of mining archaeology and, above all, a key tool to understand the Neolithisation process throughout the Adriatic region. A map of the primary and secondary chert sources of the Gargano Promontory was produced to manage field sampling of mine structures. Waste materials were selected for the geologic samples of the five mines. Due to their inaccessibility, the samples from two mines consisted exclusively of waste materials. The NM-PCI proved able to discriminate most of the analysed mines and is well suited to provenance investigations of archaeological lithic industries. Compared to previous geochemical studies, the nondestructive method proposed here improves geological resolution, allowing the discrimination of cherts within the same formation.

Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries. By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological interactions and networks that were previously unidentifiable. However, to effectively perform integrative analysis of temporal multi-omics, computational methods must account for the heterogeneity and complexity in the data. To this end, we performed unsupervised classification of proteins and metabolites in mice during cardiac remodeling using two innovative deep learning (DL) approaches. First, long short-term memory (LSTM)-based variational autoencoder (LSTM-VAE) was trained on time-series numeric data. The low-dimensional embeddings extracted from LSTM-VAE were then used for clustering. Second, deep convolutional embedded clustering (DCEC) was applied on images of temporal trends. Instead of a two-step procedure, DCEC performes a joint optimization for image reconstruction and cluster assignment. Additionally, we performed K-means clustering, partitioning around medoids (PAM), and hierarchical clustering. Pathway enrichment analysis using the Reactome knowledgebase demonstrated that DL methods yielded higher numbers of significant biological pathways than conventional clustering algorithms. In particular, DCEC resulted in the highest number of enriched pathways, suggesting the strength of its unified framework based on visual similarities. Overall, unsupervised DL is shown to be a promising analytical approach for integrative analysis of temporal multi-omics.

Globalization trend in integrated circuit design and manufacturing process has increased the vulnerability of integrated circuit. These vulnerabilities mainly caused by hardware Trojan have a serious impact on the security of integrated circuits. Although side-channel analysis approach is the most promising Trojan detection approach, nearly all side-channel analysis approaches rely heavily on the availability of golden chips, which are extremely difficult to obtain. In this paper, a golden layout model instead of fabricated golden chips is introduced for the practical application of hardware Trojan detection approaches. The simulated voltage variations generated from the golden layout model at different process corners serve as golden reference, thus fabricated golden chips are not required during detection. Further, silicon measurements are performed to obtain the voltage variations of fabricated chips, and a model calibration algorithm is utilized to calibrate the golden model in the presence of process variations and random noise. Finally, the Trojan detection is formulated as a two-class classification problem, and the Trojan is identified using the partitioning around medoids algorithm. Experimental results demonstrate that the similarities between the simulated traces and measured traces are greater than 98.81%, and the proposed approach distinguishes the Trojan chips correctly even under ± 15% process variation.

In finance, cluster analysis is a tool particularly useful for classifying stock market multivariate time series data related to daily returns, volatility daily stocks returns, commodity prices, volume trading, index, enhanced index tracking portfolio, and so on. In the literature, following different methodological approaches, several clustering methods have been proposed for clustering multivariate time series. In this paper by adopting a fuzzy approach and using the Partitioning Around Medoids strategy, we suggest to cluster multivariate financial time series by considering the dynamic time warping distance. In particular, we proposed a robust clustering method capable to neutralize the negative effects of possible outliers in the clustering process. The clustering method achieves its robustness by adopting a suitable trimming procedure to identify multivariate financial time series more distant from the bulk of data. The proposed clustering method is applied to the stocks composing the FTSE MIB index to identify common time patterns and possible outliers.

In our work, we cluster the data collected on pregnancy women through hospitals, into three different categories such as elective LSCS, emergency LSCS and normal delivery. We aim to identify outliers so that we can predict the complications that can occur during pregnancy. These complications can even lead to maternal death. Clustering is performed by the aid of k-means, and k-medoid procedures which are implemented in windows form application to run in Microsoft Visual Studio, a comparison is provided on its performance and efficiency between both the algorithms and also the outlier, i.e. normal delivery cases are detected and depicted. From our work, we can clearly say that K-Means works better when the dataset is between the range of 50 – 100. Suppose the data set is greater than 500 then K-medoid works efficiently. In our work, we are considering various maternity cases. Here we are detecting normal delivery cases as an outlier.

This paper presents a data mining study and cluster analysis of social data obtained on small producers and family farmers from six country cities in Ceará state, northeast Brazil. The analyzed data involve demographic, economic, agriculture and food insecurity information. The goal of the study is to establish profiles for the small producer families that reside in the region and to identify relevant features which differentiate these profiles. Moreover, we provide an efficient data mining methodology for analysis of social data sets which is capable of handling its natural challenges, such as mixed variables and abundance of null values. We use the Silhouette method for the estimation of the best number of natural groups within the data, along with the Partitioning Around Medoids clustering algorithm in order to compute the profiles. The Correlation-Based Feature Selection method is used to identify which social criteria are the most important to differentiate the families from each profile. Classification models based on support vector machines, multilayer perceptron and decision trees were developed aiming to predict in which of the identified clusters an arbitrary family would be best fit. We obtained a good separation of the families into two clusters, and a multilayer perceptron model with approximately 93.5% prediction accuracy. Keywords: Clustering, Social data, Classification, Pam, Data mining

This study investigates the use of clustering methodologies as a means of reducing spatio-temporal wind speed data into statistically representative classes of temporal profiles for further processing and interpretation. The clustering methodologies are applied to the high-resolution spatio-temporal, meso-scale renewable energy resource dataset produced for Southern Africa by the Council of Scientific and Industrial Research. This large dataset incorporates thousands of coordinates and represents a challenge from a computational perspective. This dataset can be reduced by applying clustering techniques to classify the temporal wind speed profiles into categories with similar statistical properties. Various clustering algorithms are considered, with the view to compare the performances of these algorithms for large wind resource datasets, namely k-means, partitioning around medoids, the clustering large applications algorithm, agglomerative clustering, the divisive analysis algorithm and fuzzy c-means clustering. Two distance measures are considered, namely the Euclidean distance and Pearson correlation distance. The validation metrics evaluated in the investigation includes the silhouette coefficient, the Calinski-Harabasz index and the Dunn index. Case study results are presented for the Komsberg Renewable Energy Development Zone, located in Western Cape, South Africa. This zone is selected based on the high mean wind speed and large standard deviation exhibited by the temporal wind speed profiles associated with the zone. The effects of seasonal variation in the temporal wind speed profiles are considered by partitioning the input dataset in accordance with the low and high demand seasons defined by the Megaflex Time of Use tariff. The clustered wind resource maps produced by the proposed methodology represent a valuable input dataset for further studies such as siting and the optimal geographical allocation of wind generation capacity to reduce the variability and ramping effects that are inherent to wind energy.

The k-medoids method is a non-hierarchical clustering to classify n object into k clusters that have the same characteristics. This clustering algorithm uses the medoid as its cluster center. Medoid is the most centrally located object in a cluster, so it’s robust to outliers. In cluster analysis the objects are grouped by the similarity. To measure the similarity, it can be used distance measures, euclidean distance and cityblock distance. The distance that is used in cluster analysis can affect the clustering results. Then, to determine the quality of the clustering results can be used the internal criteria with silhouette width and C-index. In this research the k-medoids method to classify of regencies/cities in Central Java based on type and number of crimes. The optimal cluster at k= 4 use euclidean distance, where the silhouette index= 0,3862593 and C-index= 0,043893. Keywords: Clustering, k-Medoids, Euclidean distance, Cityblock distance, Silhouette index, C-index, Crime