JACIII Vol.19 No.1 pp. 23-28
doi: 10.20965/jaciii.2015.p0023


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

April 20, 2014
August 25, 2014
January 20, 2015
assignment prototype algorithm, possibilistic clustering, L1-regularization, sequential cluster extraction, relational data
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.
Cite this article as:
Y. Hamasuna and Y. Endo, “On Cluster Extraction from Relational Data Using L1-Regularized Possibilistic Assignment Prototype Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.1, pp. 23-28, 2015.
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