Analysis of Effective Pattern Discovery with Text Mining in Business Based Application

In text documents data mining techniques have been proposed for mining useful patterns. But how to effectively use and update discovered patterns is still an open research issue, especially in the text mining. So existing text mining methods adopted term-based approaches but they all suffer from the problems of polysemy and synonymy. This paper presents an effective pattern discovery technique, which first calculates discovered specificities of patterns and then evaluates term weights according to the distribution of terms in the discovered patterns rather than the distribution in documents for solving the misinterpretation problem. It also considers the influence of patterns from the negative document examples to find noisy patterns and try to reduce their influence for the low-frequency problem. This approach can improve the accuracy of evaluating term weights because discovered patterns are more specific than whole documents