DyClee: Dynamic clustering for tracking evolving environments

作者：

Highlights：

• A dynamic clustering algorithm for tracking evolving environments is presented.

• It is able to handle non-convex, overlapping, multi-density distributions.

• Input data can be processed in batch or in stream mode to adapt to the process.

摘要

•A dynamic clustering algorithm for tracking evolving environments is presented.•It is able to handle non-convex, overlapping, multi-density distributions.•Input data can be processed in batch or in stream mode to adapt to the process.

论文关键词：Dynamic clustering,Data mining,On-line learning,Time-series,Data streams,Multi-density clustering

论文评审过程：Received 15 July 2016, Revised 16 April 2019, Accepted 14 May 2019, Available online 15 May 2019, Version of Record 29 May 2019.

论文官网地址：https://doi.org/10.1016/j.patcog.2019.05.024