International Journal of Advanced Innovative Technology in Engineering (IJAITE)

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Comparative Analysis of Association Rule Mining Based on Genetic Algorithm

Nitin C. Sabne, Dr. Shubhangi D. Sapkal

Abstract :

Association rule mining plays an important role in the various data mining process. The diversity of association rule mining spread in various fields such as market bucket analysis, medical diagnosis, and share market prediction. Nowadays various authors and researchers focus on the validation of association rule mining. For the validation of association rule mining using various optimization algorithms are used such as genetic algorithm, Ant Colony Optimization, and particle of swarm optimization also used. For the mining of rule mining, a variety of algorithms is used such as the Apriori algorithm and the tree-based algorithm. Some algorithm is wonderful performance but generate negative association rules and also suffered from multi-scan problems. This paper proposed multi-level minimum supports (MLMS-GA) association rule mining based on the min-max algorithm and MLMS formula. In this method, we used multi-level minimum supports of data tables as 0 and 1. The divided process reduces the scanning time of the database. The proposed algorithm is a combination of MLMS and min-max algorithms. The support length key is a vector value given by the transaction data set. In the process of rule optimization, we used the min-max algorithm and to evaluate, the algorithm conducted the real-world dataset such as heart disease data and some standard data used from the UCI machine learning repository.

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