An Optimized Approach on Applying Genetic Algorithm to Adaptive Cluster Validity Index

Abstract

The partitioning or clustering method is an important research branch in data mining area, and it partitions the dataset into an arbitrary number k of clusters according to the correlation attribute of all elements of the dataset. Most datasets have the original clusters number, which is estimated with cluster validity index. But most current cluster validity index methods give the error estimation for most real datasets. In order to solve this problem, this paper applies the optimization technology of genetic algorithm to the new adaptive cluster validity index, which is called the gene index (GI). The algorithm applies genetic algorithm to adjust the weight value of the valuation function of adaptive cluster validity index to train an optimal cluster validity index. The algorithm is tested with many real datasets, and results show the proposed algorithm can give higher performance and accurately estimate the original cluster number of real datasets compared with the current cluster validity index methods.

DOI: 10.1109/IIH-MSP.2007.333

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Cite this paper

@article{Sun2007AnOA, title={An Optimized Approach on Applying Genetic Algorithm to Adaptive Cluster Validity Index}, author={Lei Sun and Tzu-Chieh Lin and Hsiang-Cheh Huang and Bin-Yih Liao and Jeng-Shyang Pan}, journal={Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007)}, year={2007}, volume={2}, pages={582-585} }