Designing a New Version of Ant-miner Using Genetic Algorithm

Volume 10, Issue 2, pp 119-130 http://dx.doi.org/10.22436/jmcs.010.02.05

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Authors

Kayvan Azaryuon - Department of Computer Engineering, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran.

Abstract

The current article seeks to design and implement a new algorithm for data mining based on ant colony optimization algorithm, which is called Ant-Miner. Ant-Miner extracts classification rules from databases. In our article, we have presented a new version of Ant-Miner which is more efficient than its previous versions. The new version has been dubbed "Ant-Miner 4". We have modified the structure of the heuristic function used in Ant-Miner, implemented it based on the correction function of Laplace, and changed pheromone trail synchronization process in order to enable the redesigned system to produce rules with higher prediction power. In the proposed algorithm, we have tried to employ genetic algorithm to avoid local minimum points, produce a general optimized response, and determine the best values for the parameters. We tested Ant-Miner 4 and Ant-Miner 3 on four data sets, finding out that the new Ant-Miner has a better performance than the older version in terms of the accuracy of the extracted rules.

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Keywords

• ant colony optimization algorithm
• classification rules
• data mining
• databases.

MSC

•  68T05

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