A Pattern Taxonomy Model with New Pattern Discovery Model for Text Mining
K. Mythili, K.V. Yasodha · 2012
Most of the mining techniques are proposed for the purpose of developing efficient mining algorithms to find particular patterns within a reasonable and acceptable time frame. With a large number of patterns generated by using data mining approaches, how to effectively use and update these patterns is still an open research issue. In existing system, 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 ambiguous (noisy) patterns and try to reduce their influence for the low-frequency problem. The process of updating ambiguous patterns can be referred as pattern evolution. This approach can improve the accuracy of evaluating term weights because discovered patterns are more specific than whole documents. This technique uses two processes, pattern deploying and pattern evolving, to refine the discovered patterns in text documents. But they don’t consider the time series to rank the given sets of documents. In proposed system, the temporal text mining approach is introduced. The system terms of its ability is evaluated to predict forthcoming events in the document. Here the optimal decomposition of the time period associated with the given document set is discovered, where each subinterval consists of consecutive time points having identical information content. Extraction of sequences of 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.