A Survey on Pattern Taxonomy Model Technique for Retrieving Pattern to Classify Text Document
when a large number of research document are received, it is common to group them according to their similarities in research disciplines. The grouped document is then assigned to the appropriate experts for peer review. Current methods for grouping document are based on manual matching of similar research discipline areas and/or keywords. However, the exact research discipline areas of the document cannot often be accurately designated by the applicants due to their subjective views and possible misinterpretations. Therefore, rich information in the document full text can be used effectively. Text-mining methods have been proposed to solve the problem by automatically classifying text documents, mainly in English. Since most existing text mining methods adopted termbased approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This work presents an innovative and effective pattern retrieving technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating retrieved patterns for finding relevant and interesting text document. KeywordsComputer Networks, Network Security, Anomaly Detection, Intrusion Detection