A hybrid clustering algorithm based on Grey Wolf Optimizer and K-means algorithm
Yang Hong-guan · Journal of Jiangxi University of Science and Technology · 2015
Considering the disadvantages of K-means Cluster Algorithm, such as inadequacy for global search and being sensitive to initial cluster centers, this paper proposes a hybrid clustering algorithm based on Grey Wolf Optimizer and K-means(GWO-KM). Grey Wolf Optimizer is applied in the field of cluster analysis for the first time. With its good ability of exploration, the new intelligent algorithm helps K-means cluster find a set of cluster centers which can get the best clustering result, thus avoiding the original algorithm's over-dependence on initial centers. The experiments based on UCI show that, the hybrid algorithm can result in faster convergence speed, higher accuracy, and greater stability, compared with traditional k-means algorithm and other improved algorithms.