An Effective and Adaptable K-means Algorithm for Big Data Cluster Analysis

Abstract

Tradition K-means clustering algorithm is easy to fall into local optimum, poor clustering effect on large capacity data and uneven distribution of clustering centroids. To solve these problems, a novel k-means clustering algorithm based on Lévy flight trajectory (Lk-means) is proposed in the paper. In the iterative process of LK-means algorithm, Lévy flight is used to search new positions to avoid premature convergence in clustering. It is also applied to increase the diversity of the cluster, strengthen the global search ability of K-means algorithm, and avoid falling into the local optimal value too early. Nevertheless, the complexity of hybrid algorithm is not increased in the process of Lévy flight optimization. To verify the data clustering effect of LK-means algorithm, experiments are conducted to compare it with the k-means algorithm, XK-means algorithm, DDKmeans algorithm and Canopyk-means algorithm on 10 open source data sets. The results show that LK-means algorithm has better search results and more evenly distributed cluster centroids, which greatly improves the global search ability, big data processing ability and uneven distribution centroids of cluster of K-means algorithm.