Performing clustering analysis on collaborative models

  • Authors:

  • Hongbin Shen;Jie Yang;NingJiang Chen;Yifei Dong;Shitong Wang

  • Affiliations:

  • Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai, PR China, 200030;Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai, PR China, 200030;Philips Research East Asia, Shanghai, China, 200070;School of Computer Science and Engineering, The University of New South Wales, Australia;Department of Information Science of Southern Yangtze University, Wuxi, Jiangsu, PR China, 214036

  • Venue:
  • Intelligent Data Analysis
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

Conventional clustering algorithms are designed for a single independent dataset, i.e. Fuzzy C-Means (FCM) clustering algorithm. In the real world, a dataset is independent of other datasets but sometimes can be cooperative with others by exchanging information, such as the relationship between subsidiary companies. We should therefore consider the influence from other relative collaborative datasets while performing clustering learning under such collaborative circumstances. In this paper, three different collaborative models are discussed and new correct methods are proposed to quantitatively measure such collaboration between datasets, i.e. information gain. The corresponding collaborative clustering algorithms are presented accordingly and the theoretical analysis shows that the new cooperative clustering algorithms can finally converge to a local minimum. Experimental results demonstrate that the clustering structures obtained by new cooperative algorithms are different from those of conventional algorithms for the consideration of collaboration and the performances of these collaborative clustering algorithms can be much better than those conventional "single" clustering algorithms under the cooperating circumstances.