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Enhanced Closed Sequential Pattern Discovery for Text Mining
Prabha Selvaraj

Published in: Journal for Advance Research in Applied Sciences
Volume- 5, Issue-2, pp.428-438, Feb 2018
DPI :-> 16.10089.JARAS.2018.V5I2.428438.2347

Enormous amount of data present in the world leads to quandary in retrieval of the useful information. Various algorithms have been proposed to solve the problem of efficient use and mining of the information based on the keywords, phrases, concept of the information need. Pattern Taxonomy Model, Pattern Deploying Method and Pattern Evolving Methods suffer from the problem of low frequency which misleads the mining of useful information from large databases. To solve these problems, a new approach which uses the synset collection in closed sequential pattern mining with the confidence and length constraints. It mines the frequent closed patterns that contain no super-sequence with the same support and uses the synset collection to reduce the redundancy of synonymically similar pattern. It imposes confidence, length constraints to prune the obtained closed sequences to reduce the irrelevant data for mining the useful patterns in large database. Enhanced pattern taxonomy model improves the efficiency and retrieves the relevant information.

Key-Words / Index Term
Text Mining, Pattern Mining, Constraint based Mining, Information Retrieval.

How to cite this article
Prabha Selvaraj , “Enhanced Closed Sequential Pattern Discovery for Text Mining”, Journal for Advance Research in Applied Sciences, 5, Issue-2, pp.428-438, Feb 2018. DPI:16.10089.JARAS.V5.I2.2347