On Cluster Extraction from Relational Data Using L1-Regularized Possibilistic Assignment Prototype Algorithm
Yukihiro Hamasuna* and Yasunori Endo**
*Department of Informatics, School of Science and Engineering, Kinki University, 3-4-1 Kowakae, Higashi-osaka, Osaka 577-8502, Japan
**Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
This paper proposes entropy-based L1-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 L1-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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