Text Categorization based on Clustering Feature Selection

Abstract In this paper, we discuss a text categorization method based on k-means clustering feature selection. K-means is classical algorithm for data clustering in text mining, but it is seldom used for feature selection. For text data, the words that can express correct semantic in a class are usually good features. We use k-means method to capture several cluster centroids for each class, and then choose the high frequency words in centroids as the text features for categorization. The words extracted by k-means not only can represent each class clustering well, but also own high quality for semantic expression. On three normal text databases, classifiers based on our feature selection method exhibit better performances than original classifiers for text categorization.