An Implementation of Frequent Pattern Mining Algorithm Using Dynamic Function

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Provided by: International Journal of Computer Applications
Topic: Data Management
Format: PDF
In this paper, the authors systematically explore the search space of frequent patterns mining and represent database in transposed form. They developed an algorithm (termed DFPMT - A Dynamic Approach for Frequent Patterns Mining Using Transposition of Database) for mining frequent patterns which are based on Apriori algorithm and used Dynamic function for Longest Common Subsequence. The main distinguishing factors among the proposed schemes is the database stores in transposed form and in each iteration database is filter /reduce by generating LCS of transaction id for each pattern. Their solutions provide faster result. A quantitative exploration of these tradeoffs is conducted through an extensive experimental study on synthetic and real-life data sets.
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