An evolutionary algorithm for clustering data streams with a variable number of clusters
作者：
Highlights：
• An evolutionary algorithm for clustering data stream is proposed.
• Our algorithm allows estimating k automatically from the data in an online fashion.
• It monitors eventual degradation in the quality of the induced clusters.
• Results show our algorithm correctly detects, and react to, changes in a data stream.
• The proposed method is very competitive in terms of accuracy and time processing.
摘要
•An evolutionary algorithm for clustering data stream is proposed.•Our algorithm allows estimating k automatically from the data in an online fashion.•It monitors eventual degradation in the quality of the induced clusters.•Results show our algorithm correctly detects, and react to, changes in a data stream.•The proposed method is very competitive in terms of accuracy and time processing.
论文关键词：Evolutionary algorithms,Clustering,Data streams,Concept drift
论文评审过程：Received 2 May 2016, Revised 15 August 2016, Accepted 12 September 2016, Available online 22 September 2016, Version of Record 4 October 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.09.020
