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