Two-stage sparse representation clustering for dynamic data streams

Chen, Jie; Wang, Zhu; Yang, Shengxiang; Mao, Hua

Two-stage sparse representation clustering for dynamic data streams

dc.cclicence	CC-BY-NC-ND	en
dc.contributor.author	Chen, Jie
dc.contributor.author	Wang, Zhu
dc.contributor.author	Yang, Shengxiang
dc.contributor.author	Mao, Hua
dc.date.acceptance	2022-09
dc.date.accessioned	2022-10-04T09:47:29Z
dc.date.available	2022-10-04T09:47:29Z
dc.date.issued	2022-09-28
dc.description	The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.	en
dc.description.abstract	Data streams are a potentially unbounded sequence of data objects, and the clustering of such data is an effective way of identifying their underlying patterns. Existing data stream clustering algorithms face two critical issues: 1) evaluating the relationship among data objects with individual landmark windows of fixed size and 2) passing useful knowledge from previous landmark windows to the current landmark window. Based on sparse representation techniques, this article proposes a two-stage sparse representation clustering (TSSRC) method. The novelty of the proposed TSSRC algorithm comes from evaluating the effective relationship among data objects in the landmark windows with an accurate number of clusters. First, the proposed algorithm evaluates the relationship among data objects using sparse representation techniques. The dictionary and sparse representations are iteratively updated by solving a convex optimization problem. Second, the proposed TSSRC algorithm presents a dictionary initialization strategy that seeks representative data objects by making full use of the sparse representation results. This efficiently passes previously learned knowledge to the current landmark window over time. Moreover, the convergence and sparse stability of TSSRC can be theoretically guaranteed in continuous landmark windows under certain conditions. Experimental results on benchmark datasets demonstrate the effectiveness and robustness of TSSRC.	en
dc.funder	Other external funder (please detail below)	en
dc.funder.other	National Natural Science Foundation of China	en
dc.identifier.citation	Chen, J., Wang, Z., Yang, S. and Mao, H. (2022) Two-stage sparse representation clustering for dynamic data streams. IEEE Transactions on Cybernetics,	en
dc.identifier.doi	https://doi.org/10.1109/TCYB.2022.3204894
dc.identifier.uri	https://hdl.handle.net/2086/22226
dc.language.iso	en_US	en
dc.peerreviewed	Yes	en
dc.projectid	61303015, 61673331	en
dc.publisher	IEEE	en
dc.researchinstitute	Institute of Artificial Intelligence (IAI)	en
dc.subject	Clustering	en
dc.subject	data stream	en
dc.subject	dictionary learning	en
dc.subject	sparse representation	en
dc.title	Two-stage sparse representation clustering for dynamic data streams	en
dc.type	Article	en