Dynamic Clustering Scheme for Evolving Data Streams Based on Improved STRAP

Jinping Sui, Zhen Liu, Alex Jung, Li Liu, Xiang Li

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)
166 Downloads (Pure)


A key problem within data mining is clustering of data streams. Most existing algorithms for data stream clustering are based on quite restrictive models for the cluster dynamics. In an attempt to overcome the limitations of existing methods, we propose a novel data stream clustering method, which we refer to as improved streaming affinity propagation (ISTRAP). The ISTRAP is based on an integrated evolution detection framework which ensures that new emerging clusters are recognized timely. Moreover, within ISTRAP, outdated clusters are removed and recurrent clusters are efficiently detected rather than being treated as novel clusters. The proposed ISTRAP is non-parametric in the sense of not requiring any prior information about the number or the centers of clusters. The effectiveness of ISTRAP is evaluated using numerical experiments.
Original languageEnglish
Pages (from-to)46157-46166
Number of pages10
JournalIEEE Access
Publication statusPublished - 7 Sept 2018
MoE publication typeA1 Journal article-refereed


  • data stream clustering
  • evolving data streams
  • affinity propagation (AP)
  • on-line clustering


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