Pattern Discovery & Removal of Meaningless Patterns with FSP-Mining Algorithm

Now a days we are using digital data in database applications. So the usage of digital data is more as well as the database is more so the performance of the system decreases in terms of the speed as well as the efficiency So for that need of different text mining methods. Data-mining is the process in which we can find the relations in between the data. In this paper, we proposed a discovering frequent sequential patterns for phrases model, for phrases. It also includes finding the hidden information to extract the hidden information using different methods of data mining. Some phrases cannot be understood by the system or have different meaning so for that need of finding meaning or synonym or can say finding the hidden information which is to be extracted using data mining algorithms. However, it is difficult to use these phrases for answering what users want effectively. Therefore, we present a pattern taxonomy extraction model is used for extracting descriptive frequent patterns by pruning the meaningless. The model then is extended and is to be tested by the filtering system. At pattern-based methods outperform the keyword-based methods. that as in this system removing of meaningless patterns reduces the cost of computation & improves the efficiency of the system. The pattern based approach can improve the accuracy of system for evaluating term weights because discovered patterns are FSPecific. Also it improves the effectiveness of updating discovered patterns for finding relevant and interesting information. Proposed system efficiency will increased.