Association Rule Mining with Multi-Fitness Function Genetic Algorithm

نویسندگان

  • Mrinalini Rana
  • Ashish Ghosh
  • Bhabesh Nath
  • Manish Saggar
  • Ashish Kumar Agrawal
  • Abhimanyu Lad
چکیده

-Generating association rules is generally done by association rule mining algorithm like classical algorithm Apriori, FP-Tree Partition and so on. Genetic algorithm can also implement to generate association rules. Main advantage of Genetic algorithm is that it can perform global search. This paper presents proposed algorithm Multi-Fitness function Genetic algorithm (MFGA) based association rule mining. This proposed algorithm generates intersecting rules from dataset. A fitness function is defined for frequent itemset and then different fitness function for generating rules. Second fitness function includes some other interestingness measures than support and confidence to generate relevant rules. The proposed algorithm is compared with classical Apriori algorithm and also with existing Genetic algorithm for association rule mining on the basis of metrics Support Count.

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تاریخ انتشار 2013