Improved Prototype Text Mining for Data Knowledge Discovery

Digital data in the form of text documents is rapidly growing. Analyzing such data manually is a tedious task. Data mining techniques have been around to analyze such data and bring about interesting patterns. Many existing methods are based on term-based approaches that cant deal with synonymy and polysemy. Moreover they lack the ability in using and updating the discovered patterns. Zhong et al. proposed an effective pattern discovery technique. It discovers patterns and then computes specificities of patterns for evaluating term weights as per their distribution in the discovered patterns. It also takes care of updating patterns that exhibit ambiguity which is a feature known as pattern evolution. In this paper we implemented that technique and also built a prototype application to test the efficiency of the technique. The empirical results revealed that the solution is very useful in text mining domain.