On Cluster Extraction from Relational Data Using L₁-Regularized Possibilistic Assignment Prototype Algorithm

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  • Hamasuna Yukihiro
    Department of Informatics, School of Science and Engineering, Kinki University
  • Endo Yasunori
    Faculty of Engineering, Information and Systems, University of Tsukuba

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  • On Cluster Extraction from Relational Data Using <b><i>L<sub>1</sub></i></b>-Regularized Possibilistic Assignment Prototype Algorithm
  • On Cluster Extraction from Relational Data Using<i>L<sub>1</sub></i>-Regularized Possibilistic Assignment Prototype Algorithm

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<p>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 algorithm is 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.</p>

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