Improving the Dynamic Hierarchical Compact Clustering Algorithm by Using Feature Selection

Feature selection has improved the performance of text clustering. In this paper, a local feature selection technique is incorporated in the dynamic hierarchical compact clustering algorithm to speed up the computation of similarities. We also present a quality measure to evaluate hierarchical clustering that considers the cost of finding the optimal cluster from the root. The experimental results on several benchmark text collections show that the proposed method is faster than the original algorithm while achieving approximately the same clustering quality.