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On Sequential Cluster Extraction Based on L 1-Regularized Possibilistic Non-metric Model

  • Conference paper
Modeling Decisions for Artificial Intelligence (MDAI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8234))

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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.

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Author information

Authors and Affiliations

  1. Department of Informatics, School of Science and Engineering, Kinki University, Kowakae 3-4-1, Higashi-osaka, Osaka, 577-8502, Japan

    Yukihiro Hamasuna

  2. Faculty of Engineering, Information and Systems, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki, 305-8573, Japan

    Yasunori Endo

Authors
  1. Yukihiro Hamasuna
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  2. Yasunori Endo
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Editor information

Editors and Affiliations

  1. IIIA-CSIC, Campüus UAB s/n, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10, Naka, 186-0004, Kunitachi, Tokyo, Japan

    Yasuo Narukawa

  3. Departament d’Enginyeria de la Informació i de les Comunicacions, Universitat Autonoma de Barcelona, 08193, Bellaterra, Catalonia, Spain

    Guillermo Navarro-Arribas

  4. Internet Interdisciplinary Institute (IN3); Estudis d’Informàtica, Multimèdia i Telecomunicació, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, 08018, Barcelona, Catalonia, Spain

    David Megías

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Hamasuna, Y., Endo, Y. (2013). On Sequential Cluster Extraction Based on L 1-Regularized Possibilistic Non-metric Model. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Megías, D. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2013. Lecture Notes in Computer Science(), vol 8234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41550-0_18

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  • DOI: https://doi.org/10.1007/978-3-642-41550-0_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41549-4

  • Online ISBN: 978-3-642-41550-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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