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A Study on discovering pattern in textmining

A. Nandhini , I.Berin Jeba Jingle


Text mining is a variation on a field called data mining that tries to find interesting patterns from large databases. Many applications, such as market analysis and business management, can benefit by the use of the information and knowledge extracted from a large amount of data. Knowledge discovery can be viewed as the process of nontrivial extraction of information from large databases, information that is implicitly presented in the data, previously unknown and potentially useful for users. Using these discovered knowledge (or patterns) in the field of text mining is difficult and ineffective. The reason is that some useful long patterns with high specificity lack in support (i.e., the low-frequency problem). We argue that not all frequent short patterns are useful. Hence, misinterpretations of patterns derived from data mining techniques lead to the ineffective performance. In existing work, an effective pattern discovery technique has been proposed to overcome the low-frequency and misinterpretation problems for text mining. The proposed technique uses two processes, pattern, to refine the discovered patterns in text documents. However, the quality of the extracted terms in text documents may be not high because of lot of noise in text. To overcome these inefficiencies, a new proposed algorithm uses Hybrid approach based on the combinatorial method of MLMS-GA (Multi-level Multiple Support and Genetic Algorithm) algorithm for the mining of positive and negative item sets. To improve the effectiveness of using and updating discovered patterns to find the relevant and interesting information, the pattern discovery include pattern deploying and pattern evaluate.

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