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Multiple Kernel Based Collaborative Fuzzy Clustering Algorithm

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Intelligent Information and Database Systems (ACIIDS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9621))

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Abstract

Cluster is found as one of the best useful tools for data analysis, data mining, and pattern recognition. The FCM algorithm and its variants algorithms has been extensively used in problems of clustering or collaborative clustering. In this paper, we present a novel method involving multiple kernel technique and FCM for collaborative clustering problem. These method endowed with multiple kernel technique which transform implicitly the feature space of input data into a higher dimensional via a non linear map, which increases greatly possibility of linear separability of the patterns when the data structure of input patterns is non-spherical and complex. To evaluate the proposed method, we use the criteria of fuzzy silhouette, a sum of squared error and classification rate to show the performance of the algorithms.

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Author information

Authors and Affiliations

  1. Department of Information Systems, Le Quy Don Technical University, Hanoi, Vietnam

    Trong Hop Dang & Long Thanh Ngo

  2. Hanoi University of Industry, Bac Tu Liem, Hanoi, Vietnam

    Trong Hop Dang

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Wiltold Pedrycz

Authors
  1. Trong Hop Dang
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  2. Long Thanh Ngo
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  3. Wiltold Pedrycz
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Corresponding author

Correspondence to Long Thanh Ngo .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Wrocław University of Technology, Wrocław, Poland

    Bogdan Trawiński

  3. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

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Dang, T.H., Ngo, L.T., Pedrycz, W. (2016). Multiple Kernel Based Collaborative Fuzzy Clustering Algorithm. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_56

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  • DOI: https://doi.org/10.1007/978-3-662-49381-6_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49380-9

  • Online ISBN: 978-3-662-49381-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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