Dynamic Clustering Scheme for Evolving Data Streams Based on Improved STRAP

Jinping Sui

doi:10.1109/ACCESS.2018.2864553

Dynamic Clustering Scheme for Evolving Data Streams Based on Improved STRAP

2018-01-01

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DOI：10.1109/ACCESS.2018.2864553

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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 the 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 Moreover, within ISTRAP, outdated clusters are removed and recurrent clusters are efficiently detected rather than being treated as novel clusters.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.

Keyword:

Data stream clustering

evolving data streams

affinity propagation (AP)

on-line clustering

Dynamic Clustering Scheme for Evolving Data Streams Based on Improved STRAP

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