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An Improved Clustering Algorithm for Information Granulation

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Book cover Fuzzy Systems and Knowledge Discovery (FSKD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3613))

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Abstract

C-means clustering is a popular technique to classify unlabeled data into dif-ferent categories. Hard c-means (HCM), fuzzy c-means (FCM) and rough c-means (RCM) were proposed for various applications. In this paper a fuzzy rough c-means algorithm (FRCM) is present, which integrates the advantage of fuzzy set theory and rough set theory. Each cluster is represented by a center, a crisp lower approximation and a fuzzy boundary. The Area of a lower approximation is controlled over a threshold T, which also influences the fuzziness of the final partition. The analysis shows the proposed FRCM achieves the trade-off between convergence and speed relative to HCM and FCM. FRCM will de-grade to HCM or FCM by changing the parameter T. One of the advantages of the proposed algorithm is that the membership of clustering results coincides with human’s perceptions, which makes the method has a potential application in understandable fuzzy information granulation.

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Author information

Authors and Affiliations

  1. Harbin Institute of Technology, Harbin, 150001, China

    Qinghua Hu & Daren Yu

Authors
  1. Qinghua Hu
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  2. Daren Yu
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

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© 2005 Springer-Verlag Berlin Heidelberg

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Hu, Q., Yu, D. (2005). An Improved Clustering Algorithm for Information Granulation. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_63

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  • DOI: https://doi.org/10.1007/11539506_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28312-6

  • Online ISBN: 978-3-540-31830-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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