A new approach for weighted clustering using decision tree

In the field of cluster analysis, most clustering algorithms consider the contribution of each attribute for classification uniformly. In fact, different attributes (or different features) should be of different contribution for clustering result. In order to consider the different roles of each attribute, this paper proposes a new approach for clustering algorithms based on weights, in which decision tree technique is used to assign the weights to each attribute. The comparison results show that novel approach improves the robustness of the traditional clustering algorithms. The experimental results with various test data sets illustrate the effectiveness of the proposed approach.