On Cluster Extraction from Relational Data UsingL1-Regularized Possibilistic Assignment Prototype Algorithm | Zendy

Yukihiro Hamasuna | Zendy; Yasunori Endo | Zendy

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On Cluster Extraction from Relational Data UsingL₁-Regularized Possibilistic Assignment Prototype Algorithm

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.p0023

Subject(s) - cluster analysis , computer science , entropy (arrow of time) , data mining , algorithm , cluster (spacecraft) , artificial intelligence , physics , quantum mechanics , programming language

This paper proposes entropy-based L 1 -regularized possibilistic clustering and a method of sequential cluster extraction from relational data. Sequential cluster extraction means that the algorithm extracts cluster one by one. The assignment prototype algorithmis a typical clustering method for relational data. The membership degree of each object to each cluster is calculated directly from dissimilarities between objects. An entropy-based L 1 -regularized possibilistic assignment prototype algorithm is proposed first to induce belongingness for a membership grade. An algorithm of sequential cluster extraction based on the proposed method is constructed and the effectiveness of the proposed methods is shown through numerical examples.

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