A comparative study of feature weighting methods for document co-clustering. (1st January 2011)

• Record Type: Journal Article
• Authors: Ye, Yunming; Li, Xutao; Wu, Biao; Li, Yan
• Is Part Of: International journal of information technology, communications and convergence. Volume 1:number. 2(2011)
• Page Start: 206
• Page End: 220
• Publication Date: 2011-01-01
• Subjects: co-clustering -- feature weighting -- text clustering; Information technology -- Periodicals; Convergence (Telecommunication) -- Periodicals; 004.05
• DOI: 10.1504/IJITCC.2011.039286
• Languages: English
• ISSNs: 2042-3217

Abstract

Document clustering is an important task in data mining. Co-clustering has become one of state-of-the-art methods for this task. In this paper, we propose a feature weighting co-clustering algorithm for document co-clustering and present a comparative study on how different weighting methods affect its performance. The compared feature weighting approaches include inverse document frequency-based methods, information theory-based methods and term variance-based methods. The comparison results on benchmark data sets show that the mutual information weighting method can lead to better performance for the proposed algorithm than other weighting schemes.