时间：2023-09-16
本文（Two-Stage Sparse Representation Clustering for Dynamic Data Streams）原载IEEE Transactions on Cybernetics，由四川大学王竹教授等科研人员创作，系四川大学市场经济法治研究所系列学术成果。后续会持续分享四川大学市场经济法治研究所系列学术成果，欢迎大家阅读。
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 win
dows 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 effec
tive relationship among data objects in the landmark windows
with an accurate number of clusters. First, the proposed algo
rithm evaluates the relationship among data objects using sparse
representation techniques. The dictionary and sparse represen
tations 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 cur
rent landmark window over time. Moreover, the convergence
and sparse stability of TSSRC can be theoretically guaran
teed in continuous landmark windows under certain conditions.
Experimental results on benchmark datasets demonstrate the
effectiveness and robustness of TSSRC.
Jie Chen, Zhu Wang, Shengxiang Yang, and Hua Mao. Two-Stage Sparse Representation Clustering for Dynamic Data Streams. IEEE Transactions on Cybernetics, Vol. 53, No. 10, October 2023.（论文下载）
