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Objective‐Based Rough c‐Means Clustering
Author(s) -
Endo Yasunori,
Kinoshita Naohiko
Publication year - 2013
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21611
Subject(s) - cluster analysis , fuzzy clustering , correlation clustering , single linkage clustering , flame clustering , data mining , k medians clustering , pattern recognition (psychology) , mathematics , complete linkage clustering , artificial intelligence , cure data clustering algorithm , computer science , canopy clustering algorithm , representation (politics) , cluster (spacecraft) , fuzzy set , object (grammar) , set (abstract data type) , determining the number of clusters in a data set , fuzzy logic , politics , political science , law , programming language
Conventional clustering algorithms classify a set of objects into some clusters with clear boundaries, that is, one object must belong to one cluster. However, many objects belong to more than one cluster in real world since the boundaries of clusters generally overlap with each other. Fuzzy‐set representation of clusters makes it possible for each object to belong to more than one cluster. On the other hand, the fuzzy degree is sometimes regarded as too descriptive for interpreting clustering results. Rough‐set representation could deal with such cases. Clustering based on rough set could provide a solution that is less restrictive than conventional clustering and less descriptive than fuzzy clustering. This paper proposes a rough clustering algorithm which is based on optimization of an objective function and the calculation formula of cluster centers is the same as one by Lingras et al. Moreover, it shows effectiveness of our proposed clustering algorithm in comparison with other algorithms.