Local Feature Selection in Text Clustering

Feature selection has improved the performance of text clustering. Global feature selection tries to identify a single subset of features which are relevant to all clusters. However, the clustering process might be improved by considering different subsets of features for locally describing each cluster. In this work, we introduce the method ZOOM-IN to perform local feature selection for partitional hierarchical clustering of text collections. The proposed method explores the diversity of clusters generated by the hierarchical algorithm, selecting a variable number of features according to the size of the clusters. Experiments were conducted on Reuters collection, by evaluating the bisecting K-means algorithm with both global and local approaches to feature selection. The results of the experiments showed an improvement in clustering performance with the use of the proposed local method.