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It is commonly agreed that the quality of data analysis may be degraded by the presence of missing data. In various domains such as bioinformatics, an effective tool is required for the discovery of knowledge from gene expression datasets. One may simply ignore defected samples, while others attempt to either make an algorithm robust to the problem or develop an imputation technique to fill in missing values. This research focuses on the latter and introduces a new hybridisation of cluster- and neighbour-based references to generate an accurate estimate. It also proposes a novel exploitation of multiple clusterings as the source of cluster-wise information, instead of a single data partition that has been studied by existing methods. These data partitions are selected from a pool of base clusterings with respect to both quality and diversity criteria. Another hybridisation is thus established between swarm intelligence and this search problem. In particular, the algorithm of artificial bee colony (ABC) is explored, with two new operators being invented to allow an evolution of solutions, or food sources for bees. Also, two different imputation strategies are provided to generate entries of missing entries in a data matrix, called cluster-only and cluster-neighbour. Based on published gene expression datasets and different experimental settings, the resulting models usually outperform their baselines and recent approaches, which make use of cluster analysis or devise an intelligent determination of nearest neighbours. Furthermore, they have proven competitive to a benchmark technique belonging to the global approach, especially with high missing ratios. Further extensions to iterative refinement and supervised imputation are discussed in addition to parameter analysis. • Novel extension of multiple-clustering to unsupervised KNN based imputation approach. • A hybridisation with well-known techniques of ensemble clustering. • ABC optimisation with two new operators is employ to select clusterings from a pool. • Experiments on published gene expression datasets and compared methods. [ABSTRACT FROM AUTHOR]

This paper presents the neutrosophic genetic algorithm (NGA) to address the research gap in the application of neutrosophic theory in conjunction with genetic algorithms. NGA introduces three distinct solution spaces—truth, falsity, and indeterminacy—enabling it to entirely encompass neutrosophic solution spaces in the operational process. Fine-tuning in the true solution space (TSS), adaptive regeneration in the false solution space (FSS), and modified crossover and mutation operations in the indeterminate solution space (ISS) enhance NGA ability to navigate away from local optima while reducing computational complexity. Evaluation against several prior algorithms based on the CEC2017 test suites demonstrates the superior performance of NGA, achieving the highest overall score of 92.11% in various problems and conditions. Sensitivity analysis of NGA parameters provides significant insights into algorithm performance variations, emphasizing the substantial impact of these parameters on the NGA's performance. The application of NGA to optimize the K-means method for clustering analysis of rock discontinuity sets showcases its efficiency and potential for practical applications in related fields, highlighting its advantages over other methods. This research establishes NGA as an innovative and efficient approach to address imprecision, incompleteness, and uncertainty in practical data scenarios, with significant implications for future development and applications. [ABSTRACT FROM AUTHOR]

Highlights • The paper provides multi-view clustering algorithms for relational data. • The algorithms are based on Particle Swarm Optimization. • They are able to select the relevant views for the clustering task. • The algorithms consider eleven different fitness functions. • Experiments with real multi-view data sets shows their usefulness. Abstract Clustering of multi-view data has received increasing attention since it explores multiple views of data sets aiming at improving clustering accuracy. Particle Swarm Optimization (PSO) is a well-known population-based meta-heuristic successfully used in cluster analysis. This paper introduces two hybrid clustering methods for multi-view relational data. These hybrid methods combine PSO and hard clustering algorithms based on multiple dissimilarity matrices. These methods take advantage of the global convergence ability of PSO and the local exploitation of hard clustering algorithms in the position update step, aiming to improve the balance between exploitation and exploration processes. Moreover, the paper provides adapted versions of 11 fitness functions suitable for vector data aiming at dealing with multi-view relational data. Two performance criteria were used to evaluate the clustering quality using the two proposed methods over eleven real-world data sets including image and document data sets. Among new findings, it was observed that the top three fitness functions are Silhouette index, Xu index and Intra-cluster homogeneity. The performance of the proposed algorithms was compared with previous single and multi-view relational clustering algorithms. The results show that the proposed methods significantly outperformed the other algorithms in the majority of cases. The results reinforce the importance of the application of techniques such as PSO-based clustering algorithms in the field of expert systems and machine learning. Such application enhances classification accuracy and cluster compactness. Besides, the proposed algorithms can be useful tools in content-based image retrieval systems, providing good categorizations and automatically learning relevance weights for each cluster of images and sets of views. [ABSTRACT FROM AUTHOR]

Fog computing has emerged as a promising paradigm for distributed data processing, but managing numerous devices in fog domains is complex due to the scale of the infrastructure. To address this challenge, organizing fog devices into fog colonies allows independent management on a smaller scale. We present a genetic algorithm (GA) approach that utilizes hierarchical clustering to define the fog colony layout. The GA selects a subset of colony candidates from the dendrogram obtained with hierarchical clustering and optimizes the network communication time between users and applications and the execution time of algorithms that manage application placement in each colony. We deployed an NSGA-II, a multi-objective approach for GAs, to evaluate our proposal. Our experimental results demonstrate that combining a GA with hierarchical clustering improves both optimization objectives. We conducted nine experiment scenarios, varying the number of applications and fog devices. Our results show that even in the worst-case scenario, the GA's results dominated the solutions obtained by two control algorithms after only 137 generations. Additionally, the number of genetic solutions and their homogeneous distribution in the Pareto front were satisfactory. • Definition of a genetic algorithm for optimization of the fog colony layout. • Solution space design based on the hierarchical clustering of the infrastructure. • Adaptation of genetic operators for solutions represented as labeled dendrograms. • Hybrid fitness evaluation with a greedy algorithm for service placement. • Results support the coordinated optimization of service placement and colony layout. [ABSTRACT FROM AUTHOR]

The concept of similarity measure is fundamental for evaluating the consistency between intuitionistic fuzzy sets (IFSs). Several researchers have developed various intuitionistic fuzzy similarity measures (IFSimMs) utilizing intuitionistic fuzzy distance measures (IFDisMs) or combinations of membership and non-membership degrees of IFSs. Nevertheless, there is still ample room for the improvement, as some of these measures do not satisfy the axiomatic properties of IFSimMs and produce unreasonable results. In this paper, we first establish a dual relationship between IFDisMs and IFSimMs based on fuzzy negations, enabling the construction of an infinite number of IFSimMs from a given IFDisM. We then propose two novel IFSimMs by directly manipulating the membership and non-membership degrees of IFSs and prove that they satisfy the axiomatic properties of strict IFSimMs (SIFSimMs). Furthermore, a comparative analysis reveals that the proposed SIFSimMs do not yield any unreasonable results in various scenarios. Finally, we apply these SIFSimMs to pattern recognition, medical diagnosis, and clustering analysis problems, demonstrating their superior performance compared to some existing measures. • Show the duality between intuitionistic fuzzy distance and similarity measures. • Identify the limitations of some existing intuitionistic fuzzy similarity measures. • The properties of strict intuitionistic fuzzy similarity measure are investigated. • Pattern recognition, medical diagnosis, and clustering analysis are discussed. • Superiority of the proposed approaches is explained. [ABSTRACT FROM AUTHOR]

Cluster analysis often addresses a specific point in time, ignoring previous cluster analysis products. The present study proposes a model entitled Cluster Evolution Analysis (CEA) that addresses three phenomena likely to occur over time: (1) changes in the number of clusters; (2) changes in cluster characteristics; (3) between-cluster migration of objects. To achieve this goal, two new techniques are implemented: to find similarities between clusters at different points in time, we used the moving average of cluster centroid technique, and to detect prominent migration patterns we used the clustering of clusters technique. The research introduces two new visual tools displaying all the clusters over the entire time period under study in a single graph. The model was tested on five-year trade data of corporate bonds (2010–2014). The results obtained by the CEA model were checked and validated against the bond rating report issued periodically by the local bond rating company. The results proved the model capable of identifying repeated clusters at various points in time, and detecting patterns that predict prospective loss of value, as well as patterns that indicate stability and preservation of value over time. [ABSTRACT FROM AUTHOR]

Highlights • Depending on clustering algorithm used, the results may change. • Based on Multi-Algorithm Voting, robustness of cluster solutions can be improved. • OMAV is developed that uses an optimization algorithm as integrative method. • OMAV is applied to the example of country clustering using GLOBE data. • Increased robustness and reduced subjectivity are demonstrated. Abstract Currently, the influence of a single statistical cluster algorithm on the results of clustering procedures represents a major threat to the objectivity in clustering. To exemplify this question, this paper refers to country clustering in cross-cultural research. In this field, previous research has determined differing numbers of clusters, depending on choices available for the clustering procedure, leading to a high number of inconsistent results. Hence, it is argued that the variety in cluster solutions induced by the choice of different statistical cluster algorithms should be reduced. To this end, this study builds on Multi-Algorithm Voting (MAV) procedure introduced by Bittmann and Gelbard (2007) and presents an advancement to the MAV method. Specifically, MAV procedure is refined for the analysis of larger data sets using the simulated annealing algorithm for optimization. The use of this Optimized MAV (OMAV) is then demonstrated for country clustering in cross-cultural research. Specifically, a set of 57 countries is divided into 12 clusters based on work-related values obtained from GLOBE database reported in House et al. (2004). Thus, results clearly show that the objectivity of clustering results can be significantly improved based on OMAV. Implications for expert and intelligent systems on the use of OMAV are discussed. Namely, OMAV represents a powerful tool supporting the decision-making process in cluster analysis reducing the number of subjective and arbitrary decisions. Taken together, this study contributes to existing literature by providing an integrative and robust method of country clustering using OMAV and by presenting country clusters applicable to various settings. [ABSTRACT FROM AUTHOR]

Density-Based clustering, an important clustering analysis approach, has several advantages of discovering clusters with arbitrary shapes and identifying noise samples etc. However, existing density-based clustering approaches cannot effectively identify clusters in density-sparse region. In this paper, we propose a density-based clustering approach for dataset with density-sparse region by using density connection weight. Firstly, we define a series of new concepts, such as density connection weight and average weight. And data objects are divided into core objects, boundary objects and noise objects according to average weight, which can effectively distinguish core objects and boundary objects in density-sparse region. Secondly, a density clustering assignment strategy is constructed based on density connection weight. A density clustering algorithm, together with the dimensionality reduction using t-SNE, judiciously handles the low-density clusters in multi-density datasets and the minimal clusters in extremely imbalanced datasets. In the end, the experimental results on the UCI and synthetic datasets validate that our algorithm exhibits prominent clustering performance, especially while being adapted in diverse clustering tasks, including the multi-density, imbalanced and uniform distribution datasets. • New concepts of density connection weight etc., are defined for selecting core objects. • A filtering method of dividing data objects is proposed using average weight. • A cluster allocation strategy is constructed using density connection weight. • A density connection weight-based clustering algorithm is proposed. [ABSTRACT FROM AUTHOR]

Highlights • Analysis of virus trend using hybrid clustering. • Combining the K-means, Fuzzy C-means, and hierarchical clustering. • Processing DNA data into unsupervised learning methods. • DNA Micro Array data processing. Abstract Every single strand of DNA consists of 10 sequences of nucleotides. These sequences cannot be separated or randomly arranged because each sequence of DNA contains a certain genomic encoding. When a virus mutates, a drug or vaccine for that virus that has been given to a patient will become useless. Therefore, there is a need for a method of analysing the likely direction of DNA mutation so that preventative measures can be adapted more quickly. RNA-type viruses are able to alter the patterns of infected DNA, which is one way for such a virus to defend itself. In this paper, we propose a new hybrid clustering method that combines K-means, fuzzy C-means, and hierarchical clustering to predict the direction of DNA mutation trends. We have combined these three different approaches in a hybrid clustering method and tested it on two data sets of 1000 isolated positive hepatitis C virus (HCV)-infected and non-infected DNA strands with 37 HCV primers. We compare the results with those of eight other clustering methods, and the comparison shows that our method achieves sensitivity and specificity values of 0.998. The level of precision of cluster division is also 0.004 higher than that of the next highest among the eight methods considered for comparison. From this study, the primer trends that most often appear in isolated DNA can be found, and the origins of these trends in isolated DNA can be inferred. [ABSTRACT FROM AUTHOR]

Tail dependence of financial entities describes when the price of one financial asset has an extreme fluctuation (e.g., price sharply rises or falls), the degree of its effect on the price fluctuation of another asset. Under the background of the global financial crisis, tail dependence structure of financial entities plays an important role in financial risk management, portfolio selection, and asset pricing. In this paper, we propose a concept of tail dependence networks to investigate the tail dependence structure of the foreign exchange (FX) market. Lower- and upper-tail dependence networks for 42 major currencies in the FX market from 2005 to 2012 are constructed by combing the symmetrized Joe-Clayton copula model and two filtered graph algorithms, i.e., the minimum spanning tree (MST) and the planar maximally filtered graph (PMFG). We also construct the tail dependence hierarchical trees (HTs) associated with the MSTs to analyze the currency clusters. We find that (1) the two series of lower- and upper-tail dependence coefficients present different statistical properties; (2) the upper-tail dependence networks are tighter than the lower-tail dependence networks; and (3) different currency clusters, cliques and communities are respectively found in the two tail dependence networks. The key empirical results indicate that market participants should consider the different topological features at different market situations (e.g., a booming market or a recession market) to make decisions on the investing or hedging strategies. Overall, our obtained results based on the tail dependence networks are new insights in financial management and supply a novel analytical tool for market participants. [ABSTRACT FROM AUTHOR]

Clustering analysis is widely used as a pre-process to discover the data distribution for resampling. Existing clustering-based resampling methods mostly run unsupervised clustering on labeled data without taking advantage of the class information to guide the clustering. When there are not enough labeled data, the clustering can hardly capture the underlying data distribution. In this paper, we propose a semi-supervised hybrid resampling (SSHR) method which runs semi-supervised clustering to capture the data distribution for both over-sampling and under-sampling. Firstly, we design a semi-supervised hierarchical clustering algorithm (SSHC) which uses labeled data to guide the clustering procedure on the whole dataset. Specifically, labeled data are used to initialize a clustering model and then guide its updating via an iterative cluster-splitting process. In this way, original classes are divided into multiple disjunct clusters, which contributes o disclosing not only the inter-class imbalance but also the intra-class imbalance. Subsequently, a hybrid resampling is performed according to the result of SSHC Labeled data of the majority class are under-sampled according to the distances to their cluster centroids and the adjacency to minority cluster centroids. Furthermore, we propose a novel over-sampling approach which selects some confident unlabeled data in minority clusters as pseudo-labeled data to enlarge the training set Compared with traditional over-sampling methods, our approach contributes to discovering more about the distribution of the minority class. In order to validate the effectiveness of SSHR, we conduct extensive experiments on 44 benchmark datasets. Our method achieves the best performances in terms of both F-measure and AUC. The Friedman test demonstrates that SSHR significantly outperforms the compared state-of-the-art resampling algorithms. • Semi-supervised clustering is first introduced into imbalance learning for resampling. • We present a semi-supervised hierarchical clustering algorithm for class-splitting. • Unlabeled data in minority clusters are selectively utilized for oversampling. • Majority samples are undersampled according to the semi-supervised clustering result. • Extensive experiments demonstrate the significant effectiveness of our method. [ABSTRACT FROM AUTHOR]

This work proposes a new approach for the characterization of time series in databases (temporal databases – TDB) for temporal analysis of clusters. For the characterization of time-series it were used the level and trend components calculated through the Holt-Winters smoothing model. For the temporal analysis of those clusters, it was used in a combined manner the AGNES (Agglomerative Hierarchical Cluster) and PAM (Partition Clustering) techniques. For the application of this methodology an R-based script for generating synthetic TDBs was developed. Our proposal allows the evaluation of the clusters, both in the object movement such as in the appearance or disappearance of clusters. The model chosen to characterize the time-series is adequate because it can be applied for short periods of time in situations where changes should be promptly detected for quick decision making. [ABSTRACT FROM AUTHOR]

Cluster analysis is a useful tool used commonly in data analysis. The purpose of cluster analysis is to separate data sets into subsets according to their similarities and dissimilarities. In this paper, the fuzzy c-means algorithm was adapted for directional data. In the literature, several methods have been used for the clustering of directional data. Due to the use of trigonometric functions in these methods, clustering is performed by approximate distances. As opposed to other methods, the FCM4DD uses angular difference as the similarity measure. Therefore, the proposed algorithm is a more consistent clustering algorithm than others. The main benefit of FCM4DD is that the proposed method is effectively a distribution-free approach to clustering for directional data. It can be used for N-dimensional data as well as circular data. In addition to this, the importance of the proposed method is that it would be applicable for decision making process, rule-based expert systems and prediction problems. In this study, some existing clustering algorithms and the FCM4DD algorithm were applied to various artificial and real data, and their results were compared. As a result, these comparisons show the superiority of the FCM4DD algorithm in terms of consistency, accuracy and computational time. Fuzzy clustering algorithms for directional data (FCM4DD and FCD) were compared according to membership values and the FCM4DD algorithm obtained more acceptable results than the FCD algorithm. [ABSTRACT FROM AUTHOR]

• This paper proposes a hierarchical clustering algorithm robust to noise. • It includes a single linkage improvement that involves local density. • It forbids the merging of representative clusters. • Performance is assessed using data with known ground truth. Hierarchical clustering is widely used in data mining. The single linkage criterion is powerful, as it allows for handling various shapes and densities, but it is sensitive to noise 1 1 A sample code is available at: http://frederic.rosresearch.free.fr/mydata/homepage/.. Two improvements are proposed in this work to deal with noise. First, the single linkage criterion takes into account the local density to make sure the distance involves core points of each group. Second, the hierarchical algorithm forbids the merging of representative clusters, higher than a minimum size, once identified. The experiments include a sensitivity analysis to the parameters and a comparison of the available criteria using datasets known in the literature. The latter proved that local criteria yield better results than global ones. Then, the three single linkage criteria were compared in more challenging situations that highlighted the complementariness between the two levels of improvement: the criterion and the clustering algorithm. [ABSTRACT FROM AUTHOR]

Abstract Clustering of a group of interval-valued time series of unequal length is often encountered and the key point of this clustering is the distance measure between two interval-valued time series. However, most distance measure methods apply to interval-valued time series of equal length, and another methods applicable to unequal-length ones usually show high computational cost. In order to give a reasonable and efficient distance measure, this paper first proposes a new representation in the form of a sequence of 3-tuples for interval-valued time series. In this representation, fully take into account the time-axis and value-axis information to decrease the loss of information. Meanwhile, this representation is guaranteed to achieve dimensionality reduction. Based on the new representation, dynamic time warping algorithm is then employed and an improved dynamic time warping algorithm is produced. Furthermore, a hierarchical clustering algorithm based on the new proposed distance measure is designed for interval-valued time series of equal or unequal length. Experimental results show the effectiveness of the proposed distance and quantify the performance of the designed clustering method. [ABSTRACT FROM AUTHOR]

Highlights • Automatic tools for touristic applications are highly valuable for the user • Single-scale clustering methods are insufficient to solve real clustering problems • Multi-scale (hierarchical) density-based clustering improves landmark detection • The separation of inhabited population cores facilitates the clustering approach • Increasing the dimensionality improves the results within crowded sample spaces. Abstract The process of determining relevant landmarks within a certain region is a challenging task, mainly due to its subjective nature. Many of the current lines of work include the use of density-based clustering algorithms as the base tool for such a task, as they permit the generation of clusters of different shapes and sizes. However, there are still important challenges, such as the variability in scale and density. In this paper, we present two novel density-based clustering algorithms that can be applied to solve this: K-DBSCAN, a clustering algorithm based on Gaussian Kernels used to detect individual inhabited cores within regions; and V-DBSCAN, a hierarchical algorithm suitable for sample spaces with variable density, which is used to attempt the discovery of relevant landmarks in cities or regions. The obtained results are outstanding, since the system properly identifies most of the main touristic attractions within a certain region under analysis. A comparison with respect to the state-of-the-art show that the presented method clearly outperforms the current methods devoted to solve this problem. [ABSTRACT FROM AUTHOR]

Highlights • A novel SVM is proposed for high noise and unbalanced distribution data. • Feature selection is improved by a novel method using the hierarchical relations in feature sets. • Roulette algorithm is introduced into cooperative coevolution. Abstract In real applications, noises are often present in the obtained data, which would considerably affect the performance of machine learning models. Although support vector machine (SVM) is a classic and efficient learning model, however, it is sensitive to noises in the training data. In this paper, a novel support vector machine named as reconstructed training-set SVM (RTS-SVM) is proposed to implement classification for high-noise data, where the roulette cooperative coevolution algorithm (R-CC) is used to optimize the parameters of RTS-SVM. The proposed SVM model is applicable to make the classification of high-noise data by tackling with the sensitive effect of the ''soft margin'' of SVM on the original training set. By means of the hierarchical relations existing in feature sets, hierarchical grouping (HG) algorithm is applied to construct feature subsets, based on which R-CC coordinates the parameters of RTS-SVM to achieve the optimization of the whole model. The application of the proposed scheme in the classification of financial time series is mainly discussed. Besides, the proposed model is also verified by using synthetic data with high noises and daily life data sets. Examples are provided to illustrate the effectiveness and practicability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Highlights • Outlier detection is important for improving clustering results. • Over detection of outliers leads to information loss. • Our proposal reduces the number of over-detected outliers. • Experiments show that clustering quality can be improved, while runtime is reduced. Abstract Expert and intelligent systems use a variety of machine learning techniques to obtain and understand the information inherent in the data. Clustering is one of these techniques, which has become important and popular since it allows classifying an unlabeled dataset into clusters of similar objects. There are many clustering algorithms that have been proposed in the literature. From these algorithms, the Cross-Clustering algorithm is one of the most recent clustering algorithms for partial clustering (clustering where not necessarily all the objects are grouped into clusters), which has provided good results allowing estimating a suitable set of clusters, as well as eliminating outliers. However, this algorithm tends to eliminate too many objects as outliers, which leads to discard a lot of non-outlier objects. Additionally, the Cross-Clustering algorithms spends a lot of time evaluating several combinations of clusterings, trying to determine a suitable number of clusters. To overcome these problems, in this paper, an improved version of the Cross-Clustering algorithm (ICC) is proposed. ICC changes the clustering algorithm used for detecting outliers, as well as it modifies the way outliers are detected. Moreover, a stop criterion allowing to make a fast decision on the estimation of a suitable number of cluster, is also introduced. The performance of the improved Cross-Clustering algorithm is compared with the original algorithm on artificial and real datasets. Our results show that ICC improves the original algorithm and other state of the art clustering algorithms; in both, runtime and clustering quality. [ABSTRACT FROM AUTHOR]

Highlights • Review of existing ML-based in-silico screening models for drug discovery domain. • Creation of new feature clustering techniques to tackle high-dimensionality datasets. • Use of problem-oriented measures to improve classification accuracy. • Comparison of D2-MCS against most popular ML classifiers in the drug discovery domain. • Construction of ML models according to the intrinsic characteristic of each cluster. Abstract Machine learning methods have become an indispensable tool for utilizing large knowledge and data repositories in science and technology. In the context of the pharmaceutical domain, the amount of acquired knowledge about the design and synthesis of pharmaceutical agents and bioactive molecules (drugs) is enormous. The primary challenge for automatically discovering new drugs from molecular screening information is related to the high dimensionality of datasets, where a wide range of features is included for each candidate drug. Thus, the implementation of improved techniques to ensure an adequate manipulation and interpretation of data becomes mandatory. To mitigate this problem, our tool (called D2-MCS) can split homogeneously the dataset into several groups (the subset of features) and subsequently, determine the most suitable classifier for each group. Finally, the tool allows determining the biological activity of each molecule by a voting scheme. The application of the D2-MCS tool was tested on a standardized, high quality dataset gathered from ChEMBL and have shown outperformance of our tool when compare to well-known single classification models. [ABSTRACT FROM AUTHOR]

Highlights • Pairs of ε-hyperballs improve the effectiveness of fuzzy classification. • Clustering with pairs of prototypes efficiently supports automated fetal state assessment. • Two-step cardiotocogram analysis increases sensitivity of fetal diagnosis. Abstract Objective: In this study we propose a fuzzy classifier whose rule antecedents are determined based on the new approach to Clustering with Pairs of Prototypes (CPP). After demonstrating the high generalization ability of the classifier for six various benchmark datasets, a particular emphasis was placed on the application to support fetal state assessment based on the classification of cardiotocographic (CTG) signals. Methods: The CPP is a solution aimed at increasing the performance of fuzzy classifiers by introducing additional prototypes to those obtained using a given basal clustering method. The CPP improvement was achieved by applying the Fuzzy Clustering with ε-Hyperballs (FCεH) as basal clustering, as well as a new ant algorithm-based method of searching for pairs of prototypes. Results: The results were compared with three reference methods: the Lagrangian SVM with the Gaussian kernel function, and the same fuzzy classifier, but using the antecedents determined with the fuzzy c -means and the fuzzy (c + p) -means clustering. In case of five out of six benchmark datasets as well as for the CTG signals classification problem we achieved the highest generalization ability measured with the classification accuracy (benchmark data) and the classification quality index defined as geometric mean of sensitivity and specificity (CTG signals). Conclusions: The results of the numerical experiments showed high accuracy of the CPP-based fuzzy classifier when assessing various types of data. Moreover, the two-step classification of the CTG signals based on the proposed method allows for the efficient signal evaluation aiming to support the automated fetal state assessment. Significance and main impact: The most significant feature of the proposed method is the high generalization ability being the result of the ε-insensitive learning (FCεH clustering), while maintaining the possibility of interpreting the learning outcomes thanks to the linguistic representation of the knowledge in the form of fuzzy conditional (if-then) rules. Therefore, we believe that this solution will have a positive impact on other studies on intelligent systems. [ABSTRACT FROM AUTHOR]

Highlights • Sufficient dimension-reduction through projecting sample points on a line. • Split or merge clusters according to the density curve of the projection points. • Use split-and-merge process to adaptively give a certain number of clusters. Abstract A novel split-and-merge clustering algorithm is proposed by using projection technology and K-means method. There are two key technologies in the proposed method: shape recognition based on projection and split-and-merge process based on K-means. By projecting the data onto the connection of any two cluster centers, no matter how large the dimension of data is, we can always obtain an one-dimension density curve of the projection to guarantee an acceptable amount of calculation. Further embedded the kernel density estimate, we can determine the distribution of clusters by the shape of the one-dimensional density curve. In the split-and-merge process, this algorithm not only addresses the sensitivity in selecting initial cluster centers, but also automatically give a reasonable number of clusters. We also discuss the possibility to extend the projection split-and-merge method from K-means to density based methods (as EM algorithm and Cross-entropy clustering). Both simulation and real data experimental results show that our method performance well especially under strict data conditions. [ABSTRACT FROM AUTHOR]

This paper develops a three-step customer clustering based approach to solve two-echelon location routing problems with time windows. A bi-objective model minimizing costs and maximizing customer satisfaction is formulated along with an innovative measurement function to rank optimal solutions. The proposed methodology is a knowledge-based approach which considers customers locations and purchase behaviors, discovers similar characteristics among them through clustering, and applies exponential smoothing method to forecast periodic customers demands. We introduce a Modified Non-dominated Sorting Genetic Algorithm-II (M-NSGA-II) to simultaneously locate logistics facilities, allocate customers, and optimize the vehicle routing network. Different from many existing version of NSGA-II, our algorithm applies partial-mapped crossover as genetic operator, instead of simulated binary crossover, in order to properly handle chromosomes. The initial population is generated through a nodes’ scanning algorithm which eliminates sub-tours. Finally, to demonstrate the applicability of our mathematical model and approach, we conduct two empirical studies on generated benchmarks and the distribution network of a company in Chongqing city, China. Further comparative analyses with multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO) algorithm indicate that M-NSGA-II performs better in terms of solution quality and computation time. Results also support that: (1) the formation of clusters containing highly similar customers improves service reliability, and favors a productive customer relationship management; (2) considering product preference contributes to maximizing customer satisfaction degree and the effective control of inventories at each distribution center; (3) clustering, instead of helping to improve services, proves detrimental when too many groups are formed. Thus, decision makers need to conduct series of simulations to observe appropriate clustering scenarios. [ABSTRACT FROM AUTHOR]

A wide variety of feature selection methods have been developed as promising solutions to find the classification pattern inside increasing applications. But the exploring efficient, flexible and robust feature selection method to handle the rising big data is still an exciting challenge. This paper presents a novel hierarchical co-evolutionary clustering tree-based rough feature game equilibrium selection algorithm (CTFGES). It aims to select out the high-quality feature subsets, which can enrich the research of feature selection and classification in the heterogeneous big data. Firstly, we construct a flexible hierarchical co-evolutionary clustering tree model to speed up the process of feature selection, which can effectively extract the features from the parent and children branches of four-layer co-evolutionary clustering tree. Secondly, we design a mixed co-evolutionary game equilibrium scheme with adaptive dynamics to guide parent and children branch subtrees to approach the optimal equilibrium regions, and enable their feature sets to converge stably to the Nash equilibrium. So both noisy heterogeneous features and non-identified redundant ones can be further eliminated. Finally, the extensive experiments on various big datasets are conducted to demonstrate the more excellent performance of CTFGES, in terms of accuracy, efficiency and robustness, compared with the representative feature selection algorithms. In addition, the proposed CTFGES algorithm has been successfully applied into the feature segmentation of large-scale neonatal cerebral cortex MRI with varying noise ratios and intensity non-uniformity levels. The results indicate that it can be adaptive to derive from the cortical folding surfaces and achieves the satisfying consistency with medical experts, which will be potential significance for successfully assessing the impact of aberrant brain growth on the neurodevelopment of neonatal cerebrum. [ABSTRACT FROM AUTHOR]

Hierarchical classification is an effective approach to categorization of large-scale text data. We introduce a relaxed strategy into the traditional hierarchical classification method to improve the system performance. During the process of hierarchy structure construction, our method delays node judgment of the uncertain category until it can be classified clearly. This approach effectively alleviates the ‘block’ problem which transfers the classification error from the higher level to the lower level in the hierarchy structure. A new term weighting approach based on the Least Information Theory (LIT) is adopted for the hierarchy classification. It quantifies information in probability distribution changes and offers a new document representation model where the contribution of each term can be properly weighted. The experimental results show that the relaxation approach builds a more reasonable hierarchy and further improves classification performance. It also outperforms other classification methods such as SVM (Support Vector Machine) in terms of efficiency and the approach is more efficient for large-scale text classification tasks. Compared to the classic term weighting method TF*IDF, LIT-based methods achieves significant improvement on the classification performance. [ABSTRACT FROM AUTHOR]

This paper presents a new quantum chaotic cuckoo search algorithm (QCCS) for data clustering. Recent researches show the superiority of cuckoo search (CS) over traditional meta-heuristic algorithms for clustering problems. Unfortunately, all the cuckoos have identical search behaviours that may lead the algorithm to converge to local optima. Also, the convergence rate is sensitive to initial centroids seeds that are randomly generated. Therefore, the main contribution of this paper is to extend the CS capabilities using nonhomogeneous update inspired by the quantum theory in order to tackle the cuckoo search clustering problem in terms of global search ability. Also, the randomness at the beginning step is replaced by the chaotic map in order to make the search procedure more efficient and improve the convergence speed. In addition, an effective strategy is developed to well manage the boundaries. The experimental results on six famous real-life datasets show the significant superiority of the proposed QCCS over eight recent well known algorithms including, genetic quantum cuckoo search, hybrid cuckoo search and differential evolution, hybrid K-means and improved cuckoo search, standard cuckoo search, quantum particle swarm optimization, differential evolution, hybrid K-means chaotic particle swarm optimization and genetic algorithm for all benchmark datasets in terms of internal and external clustering quality. [ABSTRACT FROM AUTHOR]

Categorical data clustering algorithms, in contrast to numerical ones, are still in their infancy despite some algorithms have been proposed in the literature. It is known that many clustering algorithms are posed as optimization problems, where internal cluster validity functions are utilized as the objectives to find the optimal partitions. However, most of these methods consider a single criterion that can merely be applied to detect the particular structure/distribution of data. To overcome this issue, in this paper, a novel many objective fuzzy centroids clustering algorithms is proposed for categorical data using reference point based non-dominated sorting genetic algorithm, which simultaneously optimizes several cluster validity indices. In our work, an effective fuzzy centroids algorithm is employed to design the proposed approach, which is different from other contestant k -modes-type methods. Here, the fuzzy memberships are used for chromosome representation that combines with a novel genetic operation to produce new solutions. Moreover, a variable-length encoding scheme is developed for the sake of finding the clusters without knowing any prior knowledge. Experiments on several data sets demonstrate the superiority of the proposed algorithm over other state-of-the-art methods in terms of clustering accuracy and stability. On the other hand, our method can detect the cluster number if not predefined along with a desirable clustering solution. [ABSTRACT FROM AUTHOR]