On Cluster Extraction from Relational Data UsingL1-Regularized Possibilistic Assignment Prototype Algorithm
Yukihiro Hamasuna, Yasunori Endo · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2015
This paper proposes entropy-basedL1-regularized possibilistic clustering and a method of sequential cluster extraction from relational data.Sequential cluster extractionmeans 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-basedL1-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.