Clustering Analysis using a Self-organized Network Inspired by Immune Algorithm

R. Widyanto, Megawati, K. Kawamoto, and K. Hirota (Japan)


Data Mining, Clustering Analysis, Self Organized Network, Immune Algorithm


An automatic construction of neurons in neural network inspired by immune algorithm is proposed. The new network is combined with the contiguity-constrained method to perform clustering analysis. The applicability of this technique is tested with two widely referenced machine-learning cases. The experiment shows that the new technique achieved 99.33% and 100% correctness for Iris plant data and Wine recognition data respectively, better than other popular clustering methods.

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