On Sparse Possibilistic Clustering with Crispness

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

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Other Title
  • クリスプ性を持つ可能性クラスタリングについて
  • クリスプセイ オ モツ カノウセイ クラスタリング ニ ツイテ

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Abstract

In addition to fuzzy $c$-means clustering, possibilistic clustering is well-known as one of the useful techniques because it is robust against noise in data. Especially sparse possibilistic clustering is quite different from other possibilistic clustering methods in the point of membership function. We propose a way to induce the crispness in possibilistic clustering by using $L_1$-regularization and show classification function of sparse possibilistic clustering with crispness for understanding allocation rule. We, moreover, show the way of sequential extraction by proposed method. After that, we show the effectiveness of the proposed method through numerical examples.

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