An Improved Clustering Algorithm for Information Granulation

  • Qinghua Hu
  • Daren Yu
Conference paper

DOI: 10.1007/11539506_63

Volume 3613 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
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

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Qinghua Hu
    • 1
  • Daren Yu
    • 1
  1. 1.Harbin Institute of TechnologyHarbinChina