Research and Improvement on K-Means Clustering Algorithm

Abstract:

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability,and more effectively improved clustering quality and convergence speed.

• Periodical: Advanced Materials Research (Volumes 756-759)
• Pages: 3231-3235
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.3231
• Online since: September 2013
• Authors: Xue Mei Wang, Jin Bo Wang
• Keywords: Cluster Analysis, Differential Evolution (DE) Algorithm, K-Means Cluster Algorithm

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