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Optimized association rule mining using genetic algorithm

Author(s): Anandhavalli M. | Suraj Kumar Sudhanshu | Ayush Kumar | Ghose M.K.

Journal: Advances in Information Mining
ISSN 0975-3265

Volume: 1;
Issue: 2;
Start page: 01;
Date: 2009;
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Keywords: Genetic Algorithm (GA) | Association Rules | Support | Confidence | Data Mining

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 attributein 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 alongwith more than one attribute in consequent part. The major advantage of using GAs in the discovery ofprediction rules is that they perform global search and its complexity is less compared to otheralgorithms as the genetic algorithm is based on the greedy approach. The main aim of this paper is tofind all the possible optimized rules from given data set using genetic algorithm.
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