Effective Pattern Discovery for Text Mining and Compare PDM and PCM

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2016 by IJETT Journal
Volume-35 Number-5
Year of Publication : 2016
Authors : Yeshidagna Tesfaye Assegid, Rupali Gangarde
DOI :  10.14445/22315381/IJETT-V35P242


Yeshidagna Tesfaye Assegid, Rupali Gangarde"Effective Pattern Discovery for Text Mining and Compare PDM and PCM", International Journal of Engineering Trends and Technology (IJETT), V35(5),189-194 May 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Due to the fast growth of digital data and increase the specific information needs of the users, the data mining task has a vital role to extract the useful information from that large amount of data. The extraction of these data can be achieved using different data mining techniques. The main objective of doing pattern mining is to develop knowledge discovery models for the effective utilize discovered pattern and apply it in area of text mining. In data mining community, most research work focus on developing an effective pattern discovering algorithm which include technique such as sequential pattern mining frequent item mining and close sequential mining for mining useful patterns. But there is a big challenge to discover and update effective pattern. In effective pattern discovery and use techniques there are two main problems. These are: ? Low frequency and ? Pattern misinterpretation problem The general overview of a proposed system is designed to address the problems of low frequency and pattern misinterpretation of pattern discovery method. This system tries to solve the existing approach problems and compare the result generated by pattern deployment and pattern deployment wit pattern co-occurrence methods.


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Data Mining, Information Retrieval, Pattern Taxonomy Model, Text Mining, Association Rule, Sequential Pattern Mining, Close Sequential Pattern Mining, Pattern Deploying, pattern co-occurrence matrix.