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On Sequential Cluster Extraction Based on L1-Regularized Possibilistic Non-metric Model

  • Yukihiro Hamasuna
  • Yasunori Endo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8234)

Abstract

The fuzzy non-metric model is one of the clustering methods in which the membership grade of each datum to each cluster is calculated directly from dissimilarities between data. The cluster center which is referred to as representative of cluster is not used in fuzzy non-metric model. This paper discusses a new possibilistic approach for non-metric model from the viewpoint of being in the cluster. In the previous study, new possibilistic clustering and its variant have been proposed by using L 1-regularization. These possibilistic clustering methods with L 1-regularization induce a change in the membership function. Two types of non-metric model based on possibilistic approach named L 1-regularized possibilistic non-metric model are proposed in this paper. Next, the way of sequential extraction algorithm is also discussed. Moreover, the results of sequential extraction based on proposed methods are shown.

Keywords

possibilistic clustering non-metric model L1-regularization sequential cluster extraction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yukihiro Hamasuna
    • 1
  • Yasunori Endo
    • 2
  1. 1.Department of Informatics, School of Science and EngineeringKinki UniversityHigashi-osakaJapan
  2. 2.Faculty of Engineering, Information and SystemsUniversity of TsukubaTsukubaJapan

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