A novel algorithm for optimization of association rule with Karnagh map and genetic algorithm

Abstract

Association Rule mining is a very efficient technique for finding correlation among data sets. The correlation of data gives meaning full extraction process. For the mining of rules varieties of algorithms are used such as Apriori algorithm and Tree based algorithm. Some algorithm is wonder performance but generates redundant association rule and also suffered from multiple scanning problem. In this paper we proposed an algorithm for association rule mining based on Karnaugh Map and Genetic Algorithm. In this approach, support count can be calculated directly from K-Map, so no further scanning of the database is required. With genetic algorithm, a global search can be performed and found an optimal rule. We experimentally evaluate our approach, and demonstrate that our algorithm significantly reduces the computational costs and generate an optimal association rule only. For evaluating algorithm conducted the real world dataset “The National Rural Employment Guarantee Act” (NREGA) Department of Rural Development Government of India.