Clustering in large data sets with the limited memory bundle method
作者：
Highlights：
• A nonsmooth optimization based algorithm for solving the minimum sum-of-squares clustering problems is developed.
• The proposed algorithm is tested and compared to other algorithms using large real world data sets.
• The proposed algorithm is shown to be accurate and efficient for solving clustering problems in large data sets.
• Clustering problems containing hundreds of thousands of points and/or hundreds of attributes have been solved.
摘要
•A nonsmooth optimization based algorithm for solving the minimum sum-of-squares clustering problems is developed.•The proposed algorithm is tested and compared to other algorithms using large real world data sets.•The proposed algorithm is shown to be accurate and efficient for solving clustering problems in large data sets.•Clustering problems containing hundreds of thousands of points and/or hundreds of attributes have been solved.
论文关键词：Cluster analysis,Nonsmooth optimization,Nonconvex optimization,Bundle methods,Limited memory methods
论文评审过程：Received 15 December 2017, Revised 7 May 2018, Accepted 30 May 2018, Available online 31 May 2018, Version of Record 18 June 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.05.028
