HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering
作者：
Highlights：
• An efficient hybrid GA is proposed for minimum sum-of-squares (MSSC) clustering.
• It finds higher-quality local minima than K-means and state-of-the-art algorithms.
• Its computational effort grows linearly with the number of samples and clusters.
• Better local minima of the MSSC translate into better cluster validity.
• Large improvements are observable for datasets with many clusters and dimensions.
摘要
•An efficient hybrid GA is proposed for minimum sum-of-squares (MSSC) clustering.•It finds higher-quality local minima than K-means and state-of-the-art algorithms.•Its computational effort grows linearly with the number of samples and clusters.•Better local minima of the MSSC translate into better cluster validity.•Large improvements are observable for datasets with many clusters and dimensions.
论文关键词：Clustering,Minimum sum-of-squares,Global optimization,Hybrid genetic algorithm,K-means,Unsupervised learning
论文评审过程：Received 25 April 2018, Revised 30 November 2018, Accepted 16 December 2018, Available online 17 December 2018, Version of Record 27 December 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.12.022
