To Improve the Classifier Accuracy on the Text Categorization Using Soft Computing Technique

Text categorization is a conventional classification problem applied to the textual domain. It solves the problem of assigning text content to predefined categories. Automatic classification schemes can greatly facilitate the process of categorization. Categorization of documents is challenging, as the number of discriminating words can be very large. The traditional method of text categorization like KNN has a defect that the time of similarity computing is huge. In this paper, neural network technique Back propagation Layer and SOM Algorithm is proposed. The objective of this paper is to reduce the time and effort the user has to spend to find the information sought after. Keywords and phrases increase the effectiveness and efficiency of the search process. In the proposed approach, latent semantic indexing of SOM can be used to enhance the association between terms. A brief review is given on existing document clustering techniques. The proposed method will be efficient in terms of computational cost, accuracy and visualization. It can be easily adapted for large data set.