.png){kind=icon}
返回
Two-Stage Sparse Representation Clustering for Dynamic Data Streams
.png){kind=icon}
OA
AI
DOI：10.1109/TCYB.2022.3204894.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Clustering algorithms
Dictionaries
Heuristic algorithms
Machine learning
Streaming media
Data models
Convergence
Clustering
data stream
dictionary learning
sparse representation
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Self-regulation of inter-hemispheric visual cortex balance through real-time fMRI neurofeedback training.png)
NeuroImage
OAAI
NeuroImageIF0
OAAI分享收藏Statistical hierarchical clustering algorithm for outlier detection in evolving data streamsMACHINE LEARNINGOAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏
查看更多内容