International Journal of Soft Computing
ISSN: OnlineISSN: Print 1816-9503
Abstract
Association Rule Mining is a powerful method in data mining, which aims at finding latent knowledge. In other words, the objective of rule mining is the discovery of interesting patterns that exist in database but are unseen among massive volumes of data. Mining Association rules in not full of reward until it can be utilized to improve decision-making process of an organization. The main obstacle in this area is the number of rules grows exponentially with the number of items, so it arises problem of selecting interesting rules from set of rules. In this study, we attempt to optimize the rules generated by Apriori method using genetic algorithm. The generated rules may optimize using certain measures like support count, confidence factor, interestingness, comprehensibility and completeness. But for the sake of high-level rules we consider the interestingness and completeness as measures for optimization.
How to cite this article:
Sanjeev Sharma , Vivek Badhe and Sudhir Sharma . Optimization of Association Rules Using Genetic Algorithms.
DOI: https://doi.org/10.36478/ijscomp.2007.75.79
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2007.75.79