On Sequential Cluster Extraction Based onL1-Regularized Possibilisticc-Means | Zendy

Yukihiro Hamasuna | Zendy; Yasunori Endo | Zendy

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On Sequential Cluster Extraction Based onL₁-Regularized Possibilisticc-Means

Author(s) -

Yukihiro Hamasuna,

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.p0655

Subject(s) - cluster analysis , computer science , cluster (spacecraft) , entropy (arrow of time) , data mining , algorithm , fuzzy clustering , extraction (chemistry) , pattern recognition (psychology) , artificial intelligence , chemistry , physics , chromatography , quantum mechanics , programming language

Sequential cluster extraction algorithms are useful clustering methods that extract clusters one by one without the number of clusters having to be determined in advance. Typical examples of these algorithms are sequential hard c -means (SHCM) and possibilistic clustering (PCM) based algorithms. Two types of L 1 -regularized possibilistic clustering are proposed to induce crisp and possibilistic allocation rules and to construct a novel sequential cluster extraction algorithm. The relationship between the proposed method and SHCM is also discussed. The effectiveness of the proposed method is verified through numerical examples. Results show that the entropy-based method yields better results for the Rand Index and the number of extracted clusters.

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