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Software Metric Pattern Discovery for Text Mining


V.M. Gaikwad, S.S. Patil


Vol. 14  No. 5  pp. 53-56


The mining techniques are proposed for the purpose of developing effective mining algorithms to find particular patterns within reasonable and acceptable time frame. With a large number of patterns generated by using data mining approaches, how to effectively use and/or update these patterns is still an open research issue. In the existing system is an effective pattern discovery technique introduced which first calculates discovered specificity patterns and then evaluates the term weight 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 training examples to find noisy patterns and try to reduce their influence for the low-frequency problem. The process of updating uncertain modes can be referred as pattern evolution. Those approaches improve the accuracy of the evaluating term weights because discovered patterns are more specific than whole documents. This technique uses two processes, one pattern deploying and another pattern evolving, to improve the discovered patterns in text documents. But they do not consider the time series to rank the given sets of documents. In the proposed system, temporal text mining approach is introduced. The system terms of its capacity is evaluated to predict forthcoming events in the document. Here the optimal decomposition of time period associated with the given document set is discovered, where each subinterval consists of sequential time points having identical information content. Extraction of sequences of the events from new and other documents based on the publication times of these documents has been shown to be extremely effective in tracking past events


Metric pattern, mining, data mining