Analysis of Optimized Association Rule Mining Algorithm using Genetic Algorithm
Shanta Rangaswami, Shobha G., Pallavi Gupta, Anusha R. and Meghana H. Article: Analysis of Optimized Association Rule Mining Algorithm using Genetic Algorithm. IJCA Proceedings on International Conference on Information and Communication Technologies ICICT(2):12-15, October 2014. Full text available. BibTeX
@article{key:article,
author = {Shanta Rangaswami and Shobha G. and Pallavi Gupta and Anusha R. and Meghana H},
title = {Article: Analysis of Optimized Association Rule Mining Algorithm using Genetic Algorithm},
journal = {IJCA Proceedings on International Conference on Information and Communication Technologies},
year = {2014},
volume = {ICICT},
number = {2},
pages = {12-15},
month = {October},
note = {Full text available}
}
Abstract
Apriori algorithm is a classic algorithm for frequent item set mining and association rule learning over transactional databases. The algorithm determines frequent item sets, which in turn can be used to determine association rules. These rules indicate the general trends in the database. Genetic algorithm is a search heuristic that mimics the process of natural selection using a greedy approach. This heuristic is routinely used to generate useful solutions for optimization and search problems. In this paper, we apply genetic algorithm to optimize the frequent item sets generated by Apriori algorithm and identify all possible significant association rules by analyzing the working of the algorithm on real data sets.
References
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