Optimization of Association Rule Mining Apriori Algorithm Using ACO
Badri Patel1, Vijay K Chaudhari2, Rajneesh K Karan3, YK Rana4
1Badri Patel, M.Tech., Department of IT, Technocrat Institute of Technology, Bhopal, (M.P.), India.
2Prof. Vijay K Chaudhari, Head, Department of Information Technology, Truba Institute of Technology, Bhopal, (M.P.), India.
3Prof. Rajneesh K Karan, Dean Academic, Radharaman Engineering College, Bhopal (M.P.), India.
4Prof. Y.K. Rana, Head, Department of Computer Science and Engineering & Informationa Technology, Radharaman Engineering College, Bhopal (M.P.), India.
Manuscript received on February 20, 2011. | Revised Manuscript received on February 27, 2011. | Manuscript published on March 05, 2011. | PP: 24-26 | Volume-1 Issue-1, March 2011. | Retrieval Number: A008021111
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Association rule mining is an important topic in data mining field. In a given large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. Apriori algorithm that generates all significant association rules between items in the database. On the basis of the association rule mining and Apriori algorithm, this paper proposes an improved algorithm based on the Ant Colony Optimization algorithm. We can optimize the result generated by Apriori algorithm using Ant colony optimization algorithm. The algorithm improved result produces by Apriori algorithm. Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies. ACO was introduced by Dorigo and has evolved significantly in the last few years.
Keywords: Association rule mining, Apriori algorithm, Ant Colony Optimization (ACO) algorithm, data mining.