On Objective-Based Rough Hard and Fuzzyc-Means Clustering | Zendy

Naohiko Kinoshita | Zendy; Yasunori Endo | Zendy

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On Objective-Based Rough Hard and Fuzzyc-Means Clustering

Author(s) -

Naohiko Kinoshita,

Yasunori Endo

Publication year - 2015

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2015.p0029

Subject(s) - cluster analysis , computer science , correlation clustering , rough set , data mining , canopy clustering algorithm , fuzzy clustering , cure data clustering algorithm , artificial intelligence , representation (politics) , pattern recognition (psychology) , focus (optics) , machine learning , physics , optics , politics , political science , law

Clustering is one of the most popular unsupervised classification methods. In this paper, we focus on rough clustering methods based on rough-set representation. Rough k-Means (RKM) is one of the rough clustering method proposed by Lingras et al. Outputs of many clustering algorithms, including RKM depend strongly on initial values, so we must evaluate the validity of outputs. In the case of objectivebased clustering algorithms, the objective function is handled as the measure. It is difficult, however to evaluate the output in RKM, which is not objective-based. To solve this problem, we propose new objective-based rough clustering algorithms and verify theirs usefulness through numerical examples.

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