Symbolic representation of text documents using multiple kernel FCM

Abstract

In this paper, we proposed a novel method of representing text documents based on clustering of term frequency vector. In order to cluster the term frequency vectors, we make use of Multiple Kernel Fuzzy C-Means (MKFCM). After clustering, term frequency vector of each cluster are used to form a interval valued representation (symbolic representation) by the use of mean and standard deviation. Further, interval value features are stored in knowledge base as a representative of the cluster. To corroborate the efficacy of the proposed model, we conducted extensive experimentation on standard datset like Reuters- 21578 and 20 Newsgroup. We have compared our classification accuracy achieved by the Symbolic classifier with the other existing Naive Bayes classifier, KNN classifier and SVM classifier. The experimental result reveals that the classification accuracy achieved by using symbolic classifier is better than other three classifiers.