A novel and simple strategy for evolving prototype based clustering
作者：
Highlights：
• A novel strategy to evolve clusters by gradually forgetting old samples is proposed.
• It is based on a dynamic weighting schema with an adjustable “memory” parameter.
• It was used to develop evolving versions of K-means and Mixture of Models.
• The algorithms are specially geared towards data drift scenarios.
• They were tested over real data and a synthetic data drift oriented database.
摘要
•A novel strategy to evolve clusters by gradually forgetting old samples is proposed.•It is based on a dynamic weighting schema with an adjustable “memory” parameter.•It was used to develop evolving versions of K-means and Mixture of Models.•The algorithms are specially geared towards data drift scenarios.•They were tested over real data and a synthetic data drift oriented database.
论文关键词：Evolving clustering,Data stream,Concept drift,Gaussian mixture models,K-means,Cluster evolution
论文评审过程：Received 3 July 2017, Revised 27 February 2018, Accepted 19 April 2018, Available online 21 April 2018, Version of Record 15 June 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.04.020
