Fuzzy Systems and Knowledge Discovery

Volume 3613 of the series Lecture Notes in Computer Science pp 494-504

An Improved Clustering Algorithm for Information Granulation

  • Qinghua HuAffiliated withHarbin Institute of Technology
  • , Daren YuAffiliated withHarbin Institute of Technology

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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.