Miin-Shen Yang

• Chung Yuan Christian University

Intuitionistic fuzzy sets and bipolar fuzzy sets are two different ideas, used for the assessment of opinions because the intuitionistic fuzzy sets contain the membership function and non-membership function, but the bipolar fuzzy set describes opinions under two oppositional directions with the positive membership function and negative membership...

In machine learning, k-means clustering is an unsupervised leaning technique to partition the data into k clusters that are homogeneous within the cluster and heterogeneous between clusters. The k-means algorithm assigns equal importance to all features in a single view. Several multi-view clustering techniques have been developed over the last few...

The increasing effect of Internet of Things (IoT) unlocks the massive volume of the availability of Big Data in many fields. Generally, these Big Data may be in a non-independently and identically distributed fashion (non-IID). In this paper, we have contributions in such a way enable multi-view k-means (MVKM) clustering to maintain the privacy of...

The current data acquisition techniques enable the gathering and storage of extensive datasets, encompassing multidimensional arrays. Recent researchers focus on the analysis of large datasets having diverse data points. These multi-dimensional datasets comprise diverse data points and can be represented as tensors or multi-dimensional arrays. Clus...

This paper introduces a novel approach to enhance uncertainty representation, offering decision-makers a more comprehensive perspective for improved decision-making outcomes. We propose Generalized Orbicular (m,n,o) T-Spherical Fuzzy Set (GO-TSFS), a flexible extension of existing fuzzy set models including Globular T-spherical fuzzy sets (G-TSFSs)...

In the context of fuzzy relations, symmetry refers to a property where the relationship between two elements remains the same regardless of the order in which they are considered. Natural language processing (NLP) in engineering documentation discusses the application of computational methods or techniques to robotically investigate, analyze, and p...

Circular q-rung orthopair fuzzy sets (FSs) were recently considered as an extension of q-rung orthopair FSs (q-ROFSs), circular intuitionistic FSs (Cir-IFSs), and circular Pythagorean FSs (Cir-PFSs). However, they are only considered for some simple algebraic properties. In this paper, we advance the work on circular q-ROFSs (Cirq-ROFSs) in Dombi a...

Improving a risk assessment technique for the problem of cyber security is required to modify the technique’s capability to identify, evaluate, assess, and mitigate potential cyber threats and ambiguities. The major theme of this paper is to find the best strategy to improve and refine the cyber security risk assessment model. For this, we compute...

Pythagorean fuzzy hypersoft sets (PFHSSs) are a novel model that is projected to address the limitations of Pythagorean fuzzy soft sets (PFSSs) regarding the entitlement of a multi-argument domain for the approximation of parameters under consideration. It is more flexible and reliable as it considers the further classification of parameters into t...

ABSTRACT In the last few decades, there has been a significant increase in the importance of assessing social and ecological implications within industrial product supply chains. This tendency has given rise to the notion of supplier sustainability, which entails meeting the economic, environmental, and social demands of all suppliers. The supplier...

The model of Aczel-Alsina t-norm and Aczel-Alsina t-conorm is very flexible because of the involvement of a free parameter, where the algebraic norms and Drastic norms are the special cases of the Aczel-Alsina norms. Due to this reason, they are more superior and effective than other t-norms under fuzzy set theory. The major theme of this article i...

In this paper, we introduce a novel approach for computing the Hausdorff distance on T-spherical fuzzy sets (TSFSs) and subsequently formulate some powerful similarity measures (SMs) for these sets. To strengthen the theoretical foundation of the proposed measures, we provide a series of rigorous propositions and theorems to support our findings. T...

Fuzzy c-means (FCM) clustering is an extension of k-means based on the truth membership function of fuzzy sets. Neutrosophic sets extended fuzzy sets with three memberships of truth, indeterminacy, and falsity. Based on the idea of neutrosophic sets, Guo and Sengur (2015) extended FCM to the neutrosophic c-means (NCM) clustering method. The NCM alg...

The rapid development in information technology makes it easier to collect vast numbers of data through the cloud, internet and other sources of information. Multiview clustering is a significant way for clustering multiview data that may come from multiple ways. The fuzzy c-means (FCM) algorithm for clustering (single-view) datasets was extended t...

Decision-making is a complex issue, especially for attributes being more than one and further bifurcated. Correlation analysis plays an important role in decision-making problems. For neutrosophic hypersoft sets (NHSSs), they have bifurcated sub-attributes so that we cannot compare the attributive values. Thus, Correlation Coefficient (CC) should b...

Model-based clustering technique is an optimal choice for the distribution of data sets and to find the real structure using mixture of probability distributions. Many extensions of model-based clustering algorithms are available in the literature for getting most favorable results but still its challenging and important research objective for rese...

In recent days, due to the complexities of different diseases of similar types, it becomes very difficult to diagnose an accurate type of disease, and so medical diagnosis becomes a difficult task for the experts working in health departments. Many researchers try to develop new methods and techniques to over the difficulties that come across in th...

Since advanced technologies via social media, internet, virtual communities and networks and internet of things (IoT), there are more multi-view data to be collected. Multi-view clustering is a substantial tool as a natural design for clustering multi-view data. K-means (KM) clustering for (single-view) data had been extended for handling multi-vie...

In this paper, we give advanced study in complex q-rung orthopair 2-tuple linguistic variables (CQRO2-TLVs). The major theme of the paper is to evaluate the novel concept of CQRO2-TLVs and their dominant operational laws so that it can be a competent procedure to assess ambiguous and erratic information in realistic decision problems. Furthermore,...

Hausdorff distance is one of the important distance measures to study the degree of dissimilarity between two sets that had been used in various fields under fuzzy environments. Among those, the framework of single-valued neutrosophic sets (SVNSs) is the one that has more potential to explain uncertain, inconsistent and indeterminate information in...

A cubic bipolar fuzzy set (CBFS) is a robust model which has the ability to simultaneously deal with bipolarity and vagueness by taking into account both interval-valued bipolar fuzzy sets (IVBFSs) and bipolar fuzzy sets (BFSs). Motivated by this innovative model, in the present article, some novel distance and entropy measures for CBFSs are propos...

Datasets that are gathered from multiple sources are called multi-view datasets. These types of datasets represent different sets of feature attributes to form different views. Advanced computing and information technology have made it possible to collect and store a massive amount of data. As more features are added to each view, the data becomes...

Multi-attribute decision-making (MADM) is usually used to aggregate fuzzy data successfully. Choosing the best option regarding data is not generally symmetric on the grounds that it does not provide complete information. Since Aczel-Alsina aggregation operators (AOs) have great impact due to their parameter variableness, they have been well applie...

In today’s world, the countries that have easy access to energy resources are economically strong, and thus, maintaining a better geopolitical position is important. Petroleum products such as gas and oil are currently the leading energy resources. Due to their excessive worth, the petroleum industries face many risks and security threats. Observin...

In general, multivariate Gaussian distribution (MGD) is the most used probability model. However, MGD is not a good probability model for clustering under the circumstance with outliers or noisy points. In this case, multivariate t distribution (MtD) should be a better choice than MGD because MtD has more heavy tail than MGD. In this article, we pr...

Cosine and cotangent similarity measurements are critical in applications for determining degrees of difference and similarity between objects. In the literature, numerous similarity measures for various extensions of fuzzy set, soft set, Intuitionistic Fuzzy Sets (IFSs), Pythagorean Fuzzy Sets (PFSs) and HyperSoft Sets (HSSs) have been explored. N...

The subtractive clustering method (SCM), proposed by Chiu in 1994, is an effective algorithm that can be used as an approximate clustering for numerical data. Since SCM heavily depends on parameter selections of the density function, Yang and Wu in 2005 proposed a modified SCM with a modified revised density function. However, those subtractive clu...

Fuzzy c-means (FCM) clustering had been extended for handling multi-view data with collaborative idea. However, these collaborative multi-view FCM treats multi-view data under equal importance of feature components. In general, different features should take different weights for clustering real multi-view data. In this paper, we propose a novel mu...

Uncertainty is the unavoidable part of the life. In almost all circumstances, we regularly find ourselves in a state of uncertainty. Several reasons can lead to uncertainty, such as randomness, vagueness and rough knowledge. Fuzzy set (FS) theory deals with these kinds of information. Many generalizations had been made in the theory of FSs, such as...

Correlation clustering (CC) is a clustering method using a signed graph as input without specifying the number of clusters a priori. It had been widely used in real applications, such as social network and text mining. However, its exact optimization or approximate algorithms often give unsatisfactory results, especially for large-scale signed grap...

Since social media, virtual communities and networks rapidly grow, multi-view data become more popular. In general, multi-view data always contain different feature components in different views. Although these data are extracted in different ways (views) from diverse settings and domains, they are used to describe the same samples which make them...

Partitional clustering is the most used in cluster analysis. In partitional clustering, hard c-means (HCM) (or called k-means) and fuzzy c-means (FCM) are the most known clustering algorithms. However, these HCM and FCM algorithms work worse for data sets in a noisy environment and get inaccuracy when the data set has different shape clusters. For...

The k-means algorithm with its extensions is the most used clustering method in the literature. But, the k-means and its various extensions are generally affected by initializations with a given number of clusters. On the other hand, most of k-means always treat data points with equal importance for feature components. There are several feature-wei...

Clustering is a method for analyzing grouped data. Circular data were well used in various applications, such as wind directions, departure directions of migrating birds or animals, etc. The expectation & maximization (EM) algorithm on mixtures of von Mises distributions is popularly used for clustering circular data. In general, the EM algorithm i...

The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the k-means algorithm and its extensions are always influenced by initializations with a necessary...

In 1993, Krishnapuram and Keller proposed possibilistic c-means (PCM) clustering where the PCM had various extensions in the literature. However, the PCM algorithm with its extensions treats data points under equal importance for features. In real applications, different features in a data set should take different importance with different weights...

The k-means clustering algorithm is the oldest and most known method in cluster analysis. It has been widely studied with various extensions and applied in a variety of substantive areas. Since internet, social network, and big data grow rapidly, multi-view data become more important. For analyzing multi-view data, various multi-view k-means cluste...

Distance and similarity measures play a vital role to differentiate between two sets or objects. Different distance and similarity measures had been proposed for hesitant fuzzy sets (HFSs) in the literature, but either they are insufficient or not reflect desirable results. In this paper, the construction of new distance and similarity measures bet...

Fuzzy k-modes (FKM) are variants of fuzzy c-means used for categorical data. The FKM algorithms generally treat feature components with equal importance. However, in clustering process, different feature weights need to be assigned for feature components because some irrelevant features may degrade the performance of the FKM algorithms. In this pap...

Spectral clustering is a useful tool for clustering data. It separates data points into different clusters using eigenvectors corresponding to eigenvalues of the similarity matrix from a data set. There are various types of similarity functions to be used for spectral clustering. In this paper, we propose a powered Gaussian kernel function for spec...

In 1993, Krishnapuram and Keller first proposed possibilistic C-means (PCM) clustering by relaxing the constraint in fuzzy C-means of which memberships for a data point across classes sum to 1. The PCM algorithm tends to produce coincident clusters that can be a merit of PCM as a good mode-seeking algorithm, and so various extensions of PCM had bee...

In 1993, Banfield and Raftery first proposed model-based Gaussian (MB-Gauss) clustering, using eigenvalue decomposition of Gaussian covariance matrix to detect different cluster shapes. In this paper, we extend MB-Gauss to a fuzzy model-based Gaussian (F-MB-Gauss) clustering. However, the performance of both MB-Gauss and F-MB-Gauss algorithms depen...

The concept of Pythagorean fuzzy sets (PFSs) was initially developed by Yager in 2013, which provides a novel way to model uncertainty and vagueness with high precision and accuracy compared to intuitionistic fuzzy sets (IFSs). The concept was concretely designed to represent uncertainty and vagueness in mathematical way and to furnish a formalized...

Signed graphs are often used as models for social media mining, social networks analysis and nature language processing. In this paper, we study clustering algorithms for signed graphs that can be scaled for use in large-scale signed networks. A proposed mechanism, called a random walk gap (RWG), is used to extract more cluster structure informatio...

In this paper, new entropy measures for hesitant fuzzy sets (HFSs) are proposed. Measuring uncertainty for an HFS is computed by an amount of distinction between an HFS and its complement. Hausdorff metric is used to calculate a distance between an HFS and its complement which assists us to construct novel entropy of HFSs. An axiomatic definition o...

A similarity measure is a useful tool for determining the similarity between two objects. Although there are many different similarity measures among the intuitionistic fuzzy sets (IFSs) proposed in the literature, the Jaccard index has yet to be considered as way to define them. The Jaccard index is a statistic used for comparing the similarity an...

The expectation & maximization (EM) for Gaussian mixtures is popular as a clustering algorithm. However, the EM algorithm is sensitive to initial values, and so Ueda and Nakano [4] proposed the deterministic annealing EM (DA-EM) algorithm to improve it. In this paper, we investigate theoretical behaviors of the EM and DA-EM algorithms. We first der...

The Gustafson and Kessel (GK) fuzzy clustering algorithm, proposed by Gustafson and Kessel in 1979, was the first important extension to the fuzzy c-means (FCM) algorithm. Up to now, the GK algorithm had become one of the most commonly used fuzzy clustering algorithms, where the Mahalanobis distance is used as a dissimilarity measure to provide mor...

This study focuses on clustering algorithms for data on the unit hypersphere. This type of directional data lain on the surface of a unit hypersphere is used in geology, biology, meteorology, medicine and oceanography. The EM algorithm with mixtures of von Mises-Fisher distributions is often used for model-based clustering for data on the unit hype...

Group Technology (GT) is a useful tool in manufacturing systems. Cell formation (CF) is a part of a cellular manufacturing system that is the implementation of GT. It is used in designing cellular manufacturing systems using the similarities between parts in relation to machines so that it can identify part families and machine groups. Spectral clu...

To generalize the Rand index (RI) from crisp partitions to fuzzy partitions, we first propose a graph method in which color edges in the graph for crisp partitions are used to determine the relation matrix between objects such that the matrix trace can be employed to calculate the RI. This approach is then introduced into fuzzy partitions to genera...

Fuzzy clustering algorithms generally treat data points with feature components under equal importance. However, there are various data sets with irrelevant features involved in clustering process that may cause bad performance for fuzzy clustering algorithms. That is, different feature components should take different importance. In this paper, we...

Cluster ensemble has become a general technique for combining multiple clustering partitions. There are various cluster ensemble methods to be used in real applications. Recently, Zhang et al. (2012) considered a generalized adjusted Rand index ( ) for cluster ensembles by using a consensus matrix to evaluate values. However, Zhang’s method for clu...

The Jaccard and Rand indices are the best-known and used similarity measures. In general, the Jaccard index is relatively conservative, but the Rand index is relatively optimistic. In the paper, we make a generalization of Rand and Jaccard indices with its fuzzy extension. We first define a compromised weight to improve the Rand and Jaccard indices...

Mountains, which heap up by densities of a data set, intuitively reflect the structure of data points. These mountain clustering methods are useful for grouping data points. However, the previous mountain-based clustering suffers from the choice of parameters which are used to compute the density. In this paper, we adopt correlation analysis to det...

Knowing the real time of changes, called change-point, in a process is essential for quickly identifying and removing special causes. Many change-point methods in statistical process control assume the distribution and the in-control parameters of the process known, however, they are rarely known accurately. Small errors accompanied with estimated...

Directional data on a hypersphere has been used in biology, geology, medicine, meteorology and oceanography. Clustering is a useful tool for the analysis of these data on the unit hypersphere. In general, the EM algorithm with a mixture of von Mises distributions is the most commonly used clustering method for 2-dimensional directional data on the...

Change-point (CP) regression models have been widely applied in various fields, where detecting CPs is an important problem. Detecting the location of CPs in regression models could be equivalent to partitioning data points into clusters of similar individuals. In the literature, fuzzy clustering has been widely applied in various fields, but it is...

In fuzzy clustering, the fuzzy c-means (FCM) is the most known algorithm. Several extensions and variations of FCM had been proposed in the literature. The first important extension to FCM was proposed by Gustafson and Kessel (GK). In the GK fuzzy clustering, they considered the effect of different cluster shapes except for spherical shapes by repl...

In 1993, Hathaway and Bezdek combined switching regressions with fuzzy c-means (FCM) to create fuzzy c-regressions (FCR). The FCR algorithm had been widely studied and applied in various areas. However, membership of the FCR does not always correspond to the degree of belonging and it can be inaccurate in a noisy environment. Krishnapuram and Kelle...

Belief and plausibility functions based on Dempster-Shafer theory have been used to measure uncertainty. They are also widely studied and applied in diverse areas. Numerous studies in the literature have presented various generalizations of belief and plausibility functions to fuzzy sets. However, there are still less generalizations of belief and...

In this paper we propose clustering methods based on weighted quasiarithmetic means of T-transitive fuzzy relations. We first generate a T-transitive closure Rt from a proximity relation R based on a max-T composition and produce a T-transitive lower approximation or opening RT from the proximity relation R through the residuation operator. We then...

In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. However, the FCM algorithm is usually affected by initializations. Incorporating FCM into switching regressions, called the fuzzy c-regressions (FCR), has also the same drawback as FCM, where bad initializations may cause difficulties in obtaining ap...

Entropy of an intuitionistic fuzzy set (IFS) is used to indicate the degree of fuzziness for IFSs. In this paper we deal with entropies of IFSs. We first review some existing entropies of IFSs and then propose a new entropy measure based on exponential operations for an IFS. Finally, comparisons are made with some existing entropies to show the eff...

In this paper, we consider cluster analysis based on T-transitive interval-valued fuzzy relations. A fuzzy relation with its partitional tree for obtaining an agglomerative hierarchical clustering has been studied and applied. In general, these fuzzy-relation-based clustering approaches are based on real-valued memberships of fuzzy relations. Since...

This paper proposes an intuitive clustering algorithm capable of automatically self-organizing data groups based on the original data structure. Comparisons between the propopsed algorithm and EM [11. A. Banerjee, I.S. Dhillon, J. Ghosh, and S. Sra, Clustering on the unit hypersphere using von Mises-Fisher distribution, J. Mach. Learn. Res. 6 (2005...

Fuzzy clustering is generally an extension of hard clustering and it is based on fuzzy membership partitions. In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. Numerous studies have presented various generalizations of the FCM algorithm. However, the FCM algorithm and its generalizations are usually...

Similarity measures between generalized trapezoidal fuzzy numbers (GTFNs) are employed to indicate the degrees of similarity between GTFNs. Although several similarity measures of GTFNs have been proposed in the literature, none has considered using the Jaccard index. In general, the Jaccard index is a statistic used for comparing the similarity an...

Classification maximum likelihood (CML) procedure is a maximum likelihood mixture approach to clustering. In 1993, Yang first extended the CML to a so-called fuzzy CML (FCML), by combining fuzzy c-partitions with the CML function for a normal mixture model. However, normal distribution is not robust for outliers. In this paper we consider FCML with...

Eisen’s tree view is a useful tool for clustering and displaying of microarray gene expression data. In Eisen’s tree view system, a hierarchical method is used for clustering data. However, some useful information in gene expression data may not be well drawn when a hierarchical clustering is directly used in Eisen’s tree view. In this paper, we em...

The mean shift clustering algorithm is a useful tool for clustering numeric data. Recently, Chang-Chien et al. [1] proposed a mean shift clustering algorithm for circular data that are directional data on a plane. In this paper, we extend the mean shift clustering for directional data on a hypersphere.The three types of mean shift procedures are co...

In 1993, Yang first extended the classification maximum likelihood (CML) to a so-called fuzzy CML, by combining fuzzy c-partitions with the CML function. Fuzzy c-partitions are generally an extension of hard c-partitions. It was claimed that this was more robust. However, the fuzzy CML still lacks some robustness as a clustering algorithm, such as...

Similarity of intuitionistic fuzzy sets is an important measure to indicate the similarity degree between intuitionistic fuzzy sets. Based on the information carried by transforming intuitionistic fuzzy sets into their lower, upper and middle fuzzy sets, we propose a new construction for similarity measures between intuitionistic fuzzy sets. The pr...

In 2009, Yu et al. proposed a multimodal probability model (MPM) for clustering. This paper makes advanced clustering constructions on the MPM. We first reconstruct most existing clustering algorithms, such as the k-means, fuzzy c-means, possibilistic c-means, mean shift, classification maximum likelihood, and latent class methods, by establishing...

Clustering is a useful tool for finding structure in a data set. The mixture likelihood approach to clustering is a popular clustering method, in which the EM algorithm is the most used method. However, the EM algorithm for Gaussian mixture models is quite sensitive to initial values and the number of its components needs to be given a priori. To r...

Bias-corrected fuzzy c-means (BCFCM) algorithm with spatial information has been proven effective for image segmentation. It still lacks enough robustness to noise and outliers. Some kernel versions of FCM with spatial constraints, such as KFCM_S 1, KFCM_S 2 and GKFCM, were proposed to solve those drawbacks of BCFCM. However, the computational perf...

Similarity of intuitionistic fuzzy sets (IFSs) is an important measure to indicate the similarity degree between IFSs. Recently, Ye (2011) proposed a similarity measure between IFSs based on the cosine concept. Although this cosine similarity measure has good concept and merit, the measure is not satisfied the definition of a similarity between IFS...

The EM algorithm is the standard method for estimating the parameters in finite mixture models. Yang and Pan [25] proposed a generalized classification maximum likelihood procedure, called the fuzzy c-directions (FCD) clustering algorithm, for estimating the parameters in mixtures of von Mises distributions. Two main drawbacks of the EM algorithm a...

Kohonen's self-organizing map (SOM) is a competitive learning neural network that uses a neighborhood lateral interaction function to discover the topological structure hidden in the data set. It is an unsupervised learning which has both visualization and clustering properties. In general, the SOM neural network is constructed as a learning algori...

Cluster analysis is a useful tool for data analysis. Clustering methods are used to partition a data set into clusters such that the data points in the same cluster are the most similar to each other and the data points in the different clusters are the most dissimilar. The mean shift was originally used as a kernel-type weighted mean procedure tha...

In this paper we propose a robust clustering algorithm for interval data. The proposed method is based on similarity measure that is not necessary to specify a cluster number and initials. Several numerical examples demonstrate the effectiveness of the proposed robust clustering algorithm. We then apply this algorithm to the real data set with citi...

In this paper, new similarity, inclusion measure and entropy between type-2 fuzzy sets corresponding to grades of memberships are proposed. We also create the relationships among these measures between type-2 fuzzy sets. Several examples are used to present the calculation of these similarity, inclusion measure and entropy between type-2 fuzzy sets...

Similarity measures of intuitionistic fuzzy sets are used to indicate the similarity degree between intuitionistic fuzzy sets. Although several similarity measures for intuitionistic fuzzy sets have been proposed in previous studies, no one has considered the use of the Sugeno integral to define them. Since the Sugeno integral provides an expected-...

Since interval-valued memberships is better than real membership values to represent higher-order imprecision and vagueness for human perception. In this paper, we extend fuzzy relations to interval-valued fuzzy relations and then construct T-transitive interval-valued fuzzy relations for clustering. We then apply the proposed method to a practical...

In this paper, we propose a feature-weighted mountain clustering method. The proposed method can work well when there are noisy feature variables and could be useful for obtaining initial estimat of cluster centers for other clustering algorithms. Results from color image segmentation illustrate the proposed method actually produces better segmenta...

Although there have been many researches on cluster analysis considering feature (or variable) weights, little effort has been made regarding sample weights in clustering. In practice, not every sample in a data set has the same importance in cluster analysis. Therefore, it is interesting to obtain the proper sample weights for clustering a data se...

Interval-valued fuzzy set (IVFS) is an extension of fuzzy set in which it presents a fuzzy set with an interval-valued membership. A similarity measure is a useful tool for determining the similarity degree between two objects. It is also an important measure for fuzzy concepts. In this paper, we propose a new similarity measure between IVFSs. Some...

According to the concepts of consistent probability measure and resolution form, we proposed a generalized fuzzy-valued measure for the belief and plausibility functions based on the Dempster-Shafer Theory. The proposed fuzzy-valued measure of a fuzzy set can be presented in high-order interval of belief and plausibility functions and can be illust...

Similarity measures of type-2 fuzzy sets are used to indicate the similarity degree between type-2 fuzzy sets. Inclusion measures for type-2 fuzzy sets are the degrees to which a type-2 fuzzy set is a subset of another type-2 fuzzy set. The entropy of type-2 fuzzy sets is the measure of fuzziness between type-2 fuzzy sets. Although several similari...

Cellular manufacturing is a useful way to improve overall manufacturing performance. Group technology is used to increase the productivity for manufacturing high quality products and improving the flexibility of manufacturing systems. Cell formation is an important step in group technology. It is used in designing good cellular manufacturing system...

The weighted k-means proposed by Huang et al. (2005) could automatically calculate feature weights. On the other hand, the fuzzy c-means (FCM) with spatial constraints (FCM_S) is an effective algorithm suitable for image segmentation. In this paper we propose a robust exponential-distance weighted k-means (EDWkM) algorithm with spatial constraints....

In this paper, new similarity and inclusion measures between type-2 fuzzy sets are proposed. It is known that similarity measures of type-2 fuzzy sets are used to indicate the similarity degree between type-2 fuzzy sets and inclusion measures for type-2 fuzzy sets are the degrees to which a type-2 fuzzy set is a subset of another type-2 fuzzy set....

Mixtures of distributions are popularly used as probability models for analyzing grouped data. Classification maximum likelihood (CML) is an important maximum likelihood approach to clustering with mixture models. Yang et al. extended CML to fuzzy CML. Although fuzzy CML presents better results than CML, it is always affected by the fuzziness inde...