An enhanced constraint based technique for frequent itemset mining in transactional databases

 
 
 
  • Abstract
  • Keywords
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  • Abstract


    Mining frequent patterns is one of the wide area of research in recent times as it has numerous social applications.  Variety of frequent patterns finds usage in diverse applications and the research to mine those in an optimized way is an important aspect under consideration.  So far, many algorithms had been proposed for mining frequent itemsets and each has their own pros and cons.  The basic algorithms used in the process are Apriori, Fpgrowth and Eclat.  Many enhancements of these algorithms are ongoing process in recent times.  In this paper, an enhanced Varied Support Frequent Itemset (VSFIM) algorithm is proposed which is an enhancement of FPGrowth algorithm. Unique minimum support for each item in the transaction is provided and then mining is done in the proposed approach.   The performance of the proposed algorithm is tested with existing algorithms.  It is found that VSFIM outperformed the existing algorithms in both processing time and space utilization.

     

     


  • Keywords


    Itemset, FPGrowth algorithm, Frequent Patterns, support.

  • References


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Article ID: 11807
 
DOI: 10.14419/ijet.v7i2.22.11807




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