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Fuzzy Possibilistic C-Mean Clustering Algorithm for Text Categorization
Abstract
The main aim of text categorization is the classification of documents into a fixed number of predefined categories. In text categorization, the dimensionality of the feature vector is usually high. Various approaches have been proposed to reduce the dimensionality of the feature vector while performing automatic text categorization. Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. This work deals a Fuzzy Posibilistic C-Mean algorithm that reduces the dimensionality of a feature vector. It performs automatic categorization of text and hypertext documents using a Support Vector Machines (SVMs) classifier. Experimental results show that, proposed method improve the performance by reducing the number dimension required to obtain the cluster center.
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References
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