A SURVEY PAPER FOR TEXT MINING OF IMPORTANT TERM FROM RELEVANCE DOCUMENT USING PATTERN BASED MODEL

Abstract

Nowadays, the big challenge for community of Information Retrieval domain is to discover the relevance feature in text document which helps to decide whether document is relevant or irrelevant. Most existing text mining methods are based on term-based approaches which extract terms from a training set for describing relevant feature. In termbased approach multiple meaning of same word in different context leads to generate polysemy and synonymy issue. The term based approach also suffers from low level support problem. Even though pattern based text mining approach solve low level support problem but still this approach suffers from large number of noise pattern. In the propose work, a pattern discovery approach for text mining is explored. This approach discovers frequent sequential pattern and closed sequential patterns in text documents for identifying the most information contents of the documents and extract useful features for text mining. It also classifies extracted terms into three categories: positive terms, general terms, and negative terms. In this way, we update extracted features using multiple revising strategies. This technique discovers positive and negative patterns in text documents as higher level features in order to accurately weight low-level terms based on their specificity.