Clustering Analysis Based on Chaos Immune Algorithm

Wu Yanbin

Abstract


To improve the accuracy of clustering classification, the Chaos Immune Algorithm was proposed. In this algorithm, the ergodic property of chaos phenomenon is used to optimize the initial population, so it can accelerate the convergence of Immune Algorithms. Chaotic systems are sensitive to initial condition system parameters. Through the clone selection operator, antibody circulation and supplement, Clone operator and excellent individual chaotic disturbance, local optimums were avoided, so the global optimization was obtained. As for this issue, chaotic immune algorithm, ALECO-2, BPMA and BP algorithm were applied to test the algorithm performance in two group experiments. Theory and experiment showed that the Chaos Immune Algorithm can get global optimum clustering center, and greatly improve the amplitude of operation.

 

DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4426


Keywords


Chaos; immune algorithm; cluster classification

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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