Data clustering based on approach of genetic algorithm
Hai-hui Wang, Wenjie Zhao · 2008
Data clustering has been an active research area in the data mining community, and genetic algorithms have been used in a wide variety of fields to perform clustering. An efficient genetic algorithm for clustering on very large data sets is proposed in this paper. This algorithm can not only deal with higher local constringency speed and stronger global fast search, but also get down to the obstacles constraints and practicalities of large data clustering. The results on real datasets show that the algorithm performs better than the other algorithm. We also test this algorithm on artificial data sets, which are also large size. The experimental results show that our algorithm outperforms the algorithm in terms of running time as well as the quality of the clustering.