Effective pattern discovery by cleaning patterns with pattern co-occurrence matrix and PDCS deploying approach

Text mining is a discovery of interesting knowledge in text documents. Exact and accurate knowledge in the text documents needed for the user to find what they require. Many data mining methods are used to mine useful patterns from text documents. However, using and updating these discovered patterns is still an open research issue. Many term based methods are suggested, but a disadvantage with these methods is that they suffer from the problem of synonymy and polysemy. To overcome these disadvantages pattern mining methods are recommended. Pattern mining methods are not proven to be better than term based methods because of low frequency and pattern misinterpretation problem. Here an effective pattern discovery technique is given which applies a pattern co-occurrence matrix to clean close sequential patterns. Process of pattern deploying is applied with the co-occurrence weight and absolute support (PDCS) as deploying approach to overcome pattern misinterpretation problems and pattern evolving to overcome low frequency problem. It also applies a pattern co-occurrence matrix to clean close sequential patterns. This improves performance by using and updating discovered patterns and finding interesting and relevant information.