Results 161 to 170 of about 867,994 (193)

Fuzzy clustering with outliers

PeachFuzz 2000. 19th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.00TH8500), 2002
In this paper we introduce a modified objective function for fuzzy clustering. We add an additional weighting factor for each datum and derive necessary conditions for the introduced parameter in order to optimise the objective function. These conditions are used in an alternating optimisation scheme to calculate a partition of sample data.
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A new cluster-validity for fuzzy clustering

Pattern Recognition, 1999
Abstract Fuzzy cluster-validity criterion tends to evaluate the quality of fuzzy c-partitions produced by fuzzy clustering algorithms. Many functions have been proposed. Some methods use only the properties of fuzzy membership degrees to evaluate partitions. Others techniques combine the properties of membership degrees and the structure of data.
Noureddine Zahid, Mohamed Limouri, Abderrahmane Essaid   +2 more
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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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Rough–Fuzzy Collaborative Clustering

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2006
In this study, we introduce a novel clustering architecture, in which several subsets of patterns can be processed together with an objective of finding a common structure. The structure revealed at the global level is determined by exchanging prototypes of the subsets of data and by moving prototypes of the corresponding clusters toward each other ...
Sushmita Mitra, Haider Banka, Witold Pedrycz   +2 more
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Granular Prototyping in Fuzzy Clustering

IEEE Transactions on Fuzzy Systems, 2004
We introduce a logic-driven clustering in which prototypes are formed and evaluated in a sequential manner. The way of revealing a structure in data is realized by maximizing a certain performance index (objective function) that takes into consideration an overall level of matching (to be maximized) and a similarity level between the prototypes (the ...
Andrzej Bargiela, Witold Pedrycz, Kaoru Hirota   +2 more
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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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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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Correlating Fuzzy and Rough Clustering

Fundamenta Informaticae, 2012
With the gaining popularity of rough clustering, soft computing research community is studying relationships between rough and fuzzy clustering as well as their relative advantages. Both rough and fuzzy clustering are less restrictive than conventional clustering. Fuzzy clustering memberships are more descriptive than rough clustering.
Manish R. Joshi, Pawan Lingras, C. Raghavendra Rao 0001   +2 more
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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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