Results 321 to 330 of about 172,977 (352)

Cluster validity for kernel fuzzy clustering

2012 IEEE International Conference on Fuzzy Systems, 2012
This paper presents cluster validity for kernel fuzzy clustering. First, we describe existing cluster validity indices that can be directly applied to partitions obtained by kernel fuzzy clustering algorithms. Second, we show how validity indices that take dissimilarity (or relational) data D as input can be applied to kernel fuzzy clustering.
Timothy C. Havens, James C. Bezdek, Marimuthu Palaniswami   +2 more
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A cluster validity index for fuzzy clustering

Pattern Recognition Letters, 2005
Cluster validity indexes have been used to evaluate the fitness of partitions produced by clustering algorithms. This paper presents a new validity index for fuzzy clustering called a partition coefficient and exponential separation (PCAES) index. It uses the factors from a normalized partition coefficient and an exponential separation measure for each
Kuo-Lung Wu, Miin-Shen Yang
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Fuzzy rule clustering

2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236), 2002
The concept of relevance has been proposed as a measure of the relative importance of sets of rules, allowing the development of a new methodology for organising the linguistic information: SLIM (Separation of Linguistic Information Methodology). Based on this concept and on this methodology, a new fuzzy clustering of fuzzy rules algorithm (FCFRA) is ...
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FUZZY CLUSTERING BASED ON INTUITIONISTIC FUZZY RELATIONS

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004
It is well known that an intuitionistic fuzzy relation is a generalization of a fuzzy relation. In fact there are situations where intuitionistic fuzzy relations are more appropriate. This paper discusses the fuzzy clustering based on intuitionistic fuzzy relations. On the basis of max -t & min -s compositions, we discuss an n-step procedure which
Wen-Liang Hung, Jinn-Shing Lee, Cheng-Der Fuh   +2 more
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Fuzzy multisets and fuzzy clustering of documents

10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297), 2002
Aims at developing a method of fuzzy clustering based on fuzzy multisets. Data clustering has been discussed in relation to information retrieval models and fuzzy multisets provide an appropriate model of information retrieval on the WWW. Fuzzy clustering of fuzzy multisets is thus necessary for application to an information retrieval model.
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Fuzzy Agglomerative Clustering

2015
In this paper, we describe fuzzy agglomerative clustering, a brand new fuzzy clustering algorithm. The basic idea of the proposed algorithm is based on the well-known hierarchical clustering methods. To achieve the soft or fuzzy output of the hierarchical clustering, we combine the single-linkage and complete-linkage strategy together with a fuzzy ...
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Genetic algorithms for clustering and fuzzy clustering

WIREs Data Mining and Knowledge Discovery, 2011
AbstractClustering has been an area of intensive research for several decades because of its multifaceted applications in innumerable domains. Clustering can be either Boolean, where a single data point belongs to exactly one cluster, or fuzzy, where a single data point can have nonzero belongingness to more than one cluster.
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A cluster validity index for fuzzy clustering

Fuzzy Sets and Systems, 2010
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Fuzzy Clustering of Intuitionistic Fuzzy Data

2012
In the paper a new method of fuzzy clustering basing on fuzzy features is presented. Objects are described by set of features with intutionistic fuzzy values. Generally, the method uses the concept of modified fuzzy c-means procedure applied to intuitionistic fuzzy data which describes the features. New distance measure between data and cluster centers
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Robust Fuzzy Clustering with Fuzzy Data

2005
Proposed method of clustering is based on modified fuzzy c-means algorithm. In the paper features of input data are considered as linguistic variables. Any feature is described by set of fuzzy numbers. Thus, any input data representing a feature is a fuzzy number. The modified method allows finding the appropriate number of classes.
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