Study on New Information Theory Based Cooperative Clustering Algorithm
Hong Shen · Chinese Journal of Computers · 2005
Conventional clustering algorithms are designed for a single independent dataset, e.g. Fuzzy C-Means (FCM) clustering algorithm. In real world, a dataset is independent of other datasets but sometimes can be cooperative with others by exchanging information, such as the relationship between the subsidiary companies. So the influence from other relative collaborative datasets should be considered while performing clustering learning under such collaborative circumstances. Two different collaborative models are discussed and new proper methods are proposed to quantitatively measure such collaboration between datasets in this paper, e.g. information gain. The corresponding collaborative clustering algorithms are presented accordingly and the theoretic analysis shows that the new cooperative clustering algorithms can finally converge to 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.