Semi Supervised Document Classification Model Using Artificial Neural Networks

Automatic document classification is of paramount importance to knowledge management in the information age. Document classification is a kind of text data mining and organization technique that automatically groups related documents into clusters. Most of the common techniques in document classification are based on the statistical analysis of a term, either word or phrase. Statistical analysis of a term frequency captures the importance of the term within the document only. However, two terms can have the same frequency in their documents, but one term contributes more to the meaning of its sentences than the other term. To solve this problem the proposed system concentrates on an interactive text clustering methodology, semi supervised document classification method using neural networks. There are two main phases in the proposed method: Pre-processing phase and Classification phase. In the pre-processing phase, distinct words are identified and their frequency of occurrences in the document corpus is calculated. These discovered distinct words with their frequency of occurrences, form a document vector. In the classification phase, Back propagation algorithm is used for document classification by using the feature vector of distinct words. The proposed method evaluates the system efficiency by implementing and testing the clustering results with Dbscan and Kmeans clustering algorithms. Experiment shows that the proposed document clustering method performs with an average efficiency of 92% for various document categories..