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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.

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

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