Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Context-sensitive learning methods for text categorization
ACM Transactions on Information Systems (TOIS)
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Automated Variable Weighting in k-Means Type Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linguistic models and linguistic modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A document can only be partitioned into one class by the most clustering methods, but one document can fall into several classes and have multilabel in practice. A method to solve this problem is one to use fuzzy clustering. However, the conventional fuzzy clustering methods greatly depend on priori parameters, being parameter-sensitive. In this paper, a new fuzzy clustering method is used for dealing with multilabel text categorization. Unlike the conventional fuzzy clustering methods, the new method constrains each row rather than each column of fuzzy partition matrix associated with the objective function. Therefore, the parameter-sensitive problem greatly is overcome. The experimental results on the real-world text datasets show that the method can work faster and better compared with the conventional methods.