Hybrid Approach to Improve Pattern Discovery in Text mining

Abstract: Text clustering can greatly simplify browsing large collections of documents by reorganizing them into a smaller number of patterns in text documents manageable clusters. Text clustering is mainly used for a document clustering system which clusters the set of documents based on the user typed key term. Here we proposed a hybrid model which works on reduced dimensional dataset and similarity constraints. Feature based analysis is used for reducing dimension of huge dataset. We use the feature evaluation to reduce the dimensionality of high-dimensional text vector. The system then identifies the term frequency and then those frequencies are weighted by using the inverted document frequency method. Then this weight of documents is used for clustering. Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. This model will significantly improve the result of pattern discovery in text mining.

Keywords: text mining, text classification, pattern mining, pattern deploying, pattern evolving