OPTIMIZED ASSOCIATION RULE MINING USING GENETIC ALGORITHM

Anandhavalli M.¹*, Suraj Kumar Sudhanshu²*, Ayush Kumar³, Ghose M.K.⁴*
¹Department of Computer Science Engineering, Sikkim Manipal Institute of Technology, East Sikkim, India
²Department of Computer Science Engineering, Sikkim Manipal Institute of Technology, East Sikkim, India
³Department of Computer Science Engineering, Sikkim Manipal Institute of Technology, East Sikkim, India
⁴Department of Computer Science Engineering, Sikkim Manipal Institute of Technology, East Sikkim, India
* Corresponding Author : mkghose2000@yahoo.com

Received : -     Accepted : -     Published : 21-12-2009
Volume : 1     Issue : 2       Pages : 1 - 4
Adv Inform Min 1.2 (2009):1-4

Keywords : Genetic Algorithm (GA), Association Rules, Support, Confidence, Data Mining
Conflict of Interest : None declared

In general the rule generated by association rule mining algorithms like priori, partition, pincer-search, incremental, border algorithm etc, does not consider negation occurrence of the attribute in them and also these rules have only one attribute in the consequent part. By using Genetic Algorithm (GAs) the system can predict the rules which contain negative attributes in the generated rules along with more than one attribute in consequent part. The major advantage of using GAs in the discovery of prediction rules is that they perform global search and its complexity is less compared to other algorithms as the genetic algorithm is based on the greedy approach. The main aim of this paper is to find all the possible optimized rules from given data set using genetic algorithm.

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