Text Classification is a challenging and a red hot field in the currentscenario and has great importance in text categorization applications. A lot ofresearch work has been done in this field but there is a need to categorize acollection of text documents into mutually exclusive categories by extractingthe concepts or features using supervised learning paradigm and differentclassification algorithms. In this paper, a new Fuzzy Similarity Based ConceptMining Model (FSCMM) is proposed to classify a set of text documents into pre -defined Category Groups (CG) by providing them training and preparing on thesentence, document and integrated corpora levels along with feature reduction,ambiguity removal on each level to achieve high system performance. FuzzyFeature Category Similarity Analyzer (FFCSA) is used to analyze each extractedfeature of Integrated Corpora Feature Vector (ICFV) with the correspondingcategories or classes. This model uses Support Vector Machine Classifier (SVMC)to classify correctly the training data patterns into two groups; i. e., + 1and - 1, thereby producing accurate and correct results. The proposed modelworks efficiently and effectively with great performance and high - accuracyresults.
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