Document Classification Using Part of Speech in Text Mining

Text mining is a practice that is used to find beneficial in arrangement from the large amount of data sets. Data mining has guidelines called as frequent pattern and association rule that is important for finding frequent patterns. Text Mining is the detection by computer of new, previously unidentified in arrangement by automatically mining in arrangement from dissimilar written resources. Text mining methods are the fundamental and permitting tools for efficient organization, triangulation, retrieval and summarization of large document quantity. The problem is more often than not decomposed into two sub problems. The first is to find those kind of item sets whose occurrence goes beyond a predefined threshold set in the database, those item sets are describe frequent or large item sets. The second problem is to produce involvement rules from those huge item sets with the restriction of minimal self-confidence. In this work, the text mining is done by dividing the given set of paragraphs into tokens and classifying them accordingly. The techniques are purely composed of sequential pattern mining, closed pattern mining & frequent pattern mining. Hence, the discovered patterns in the field of text mining cannot be used further or again. All frequently used short patterns are not useful here. In this work, an effective pattern taxonomy model & part of speech have been proposed to overcome and solve the problem of low frequency & misinterpretation.