FCM-Based clustering algorithm ensemble for large data sets

  • Authors:

  • Jie Li;Xinbo Gao;Chunna Tian

  • Affiliations:

  • School of Electronic Engineering, Xidian Univ., Xi'an, P.R. China;School of Electronic Engineering, Xidian Univ., Xi'an, P.R. China;School of Electronic Engineering, Xidian Univ., Xi'an, P.R. China

  • Venue:
  • FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
  • Year:
  • 2006

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Abstract

In the field of cluster analysis, most of the available algorithms were designed for small data sets, which cannot efficiently deal with large scale data set encountered in data mining. However, some sampling-based clustering algorithms for large scale data set cannot achieve ideal result. For this purpose, a FCM-based clustering ensemble algorithm is proposed. Firstly, it performs the atom clustering algorithm on the large data set. Then, randomly select a sample from each atom as representative to reduce the data amount. And the ensemble learning technique is used to improve the clustering performance. For the complex large data sets, the new algorithm has high classification speed and robustness. The experimental results illustrate the effectiveness of the proposed clustering algorithm.