Application of Modified Artificial Fish Swarm Algorithm for Optimizing Association Rule Mining

T  Bharathi  and P  Krishnakumari

doi:10.17485/ijst/2014/v7i12.4

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Application of Modified Artificial Fish Swarm Algorithm for Optimizing Association Rule Mining

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2014/v7i12.4

Year: 2014, Volume: 7, Issue: 12, Pages: 1906–1915

Original Article

Application of Modified Artificial Fish Swarm Algorithm for Optimizing Association Rule Mining

T. Bharathi* and P. Krishnakumari

RVS Arts and Science College, Coimbatore, Tamilnadu-641402 India; pbharathi36@gmail.com, kjagadeesh@yahoo.com

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

We present a Modified Artificial Fish Swarm Algorithm (MFSA) which has many benefits that includes higher convergence rate, flexibility, fault tolerance and high accuracy. General behaviors systems of standard AFSA are: Prey, Follow, and Swarm. From the experimental results, we can say that our proposed system such as the optimized by Modified AFSA (MFSA) is better than that of PSO algorithm. Obviously, the feasibility of MAFSA based optimization method and the better global search capability of the AFSA have been proved.

Keywords: Data Mining, Particle Swarm Optimization, Modified Artificial Fish Swarm Algorithm, Minimal Support and Confidence, Global Search