Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 20, Issue 5, Pages 641-652Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2007.190740
Keywords
text clustering; text mining; chi(2) Statistic; feature selection; performance analysis
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Feature, selection is an important method for improving the efficiency and accuracy of text categorization algorithms by removing redundant and irrelevant terms from the corpus. In this paper, we propose a new supervised feature selection method, named CHIR, which is based on the chi(2) statistic and new statistical data that can measure the positive term-category dependency. We also propose a new text clustering algorithm, named Text Clustering with Feature Selection (TCFS). TCFS can incorporate CHIR to identify relevant features (i.e., terms) iteratively, and the clustering becomes 6 learning process. We compared TCFS and the K-means clustering algorithm in combination with different feature selection methods for various real data sets. Our experimental results show that TCFS with CHIR has better clustering accuracy in terms of the F-measure and the purity.
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