Discriminative Features Selection in Text Mining Using TF - IDF Scheme

This paper describes technique for discriminative features selection in Text mining. 'Text mining’ is the discovery of new, previously unknown information, by computer. Discriminative features are the most important keywords or terms inside document collection which describe the informative news included in the document collection. Generated keyword set are used to discover Association Rules amongst keywords labeling the document. For feature extraction Information Retrieval Scheme i.e. TF-IDF is used. This system uses previous work, which contains Text Preprocessing Phases (filtration and stemming). This work serves as basis for Association Rule Mining Phase. Association rule mining represents a Text Mining technique and its goal is to find interesting association or correlation relationships among a large set of data items. With massive amounts of data continuously being collected and stored in databases, many companies are becoming interested in mining association rules from their databases to increase their profits Knowledge discovery in databases (KDD) is the process of finding useful information and pattern in data. Keywords-Data Mining, Text Mining, Knowledge Data Discovery, Association Rules.