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Scalable Subspace Learning for Clustering Data Streams
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Hongwei YIN1, 2, 3, Yuzhou NI1, 2, Wenjun HU1, 2, 3
Telecommunication Engineering | 2025, 65(11) : 1836 - 1843
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Telecommunication Engineering | 2025, 65(11): 1836-1843
• Application Fundamental Research and Advanced Technology •
Scalable Subspace Learning for Clustering Data Streams
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Hongwei YIN1, 2, 3, Yuzhou NI1, 2, Wenjun HU1, 2, 3
Affiliations
  • 1School of Information Engineering，Huzhou University，Huzhou 31300，China
  • 2Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000,China
  • 3Huzhou Key Laboratory of Aquatic Robot Technology，Huzhou 313000，China
Published: 2025-11-28 doi: 10.20079/j.issn.1001-893x.240618002
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Traditional data stream clustering methods lack online dimensionality reduction capabilities for high-dimensional data, leading to limited clustering performance. To address this issue,a Scalable Subspace Learning for Clustering Data Streams(S2LCStream) method is proposed. Firstly,this method establishes a projection relationship between historical data and new data through scalable subspace learning,projecting the new data into the subspace spanned by historical data to obtain its clustering assignment in real-time. Secondly,to maintain the accuracy of clustering assignments over time, the method performs consistency detection of data distribution on the continuously arriving data stream,capturing concept drifts and adjusting clustering assignments through a backtracking mechanism to adapt to dynamically changing data distributions. Finally,the proposed method is validated on multiple real-world datasets, demonstrating its efficiency in handling high-dimensional data streams. Specifically, S2LCStream maintains high clustering accuracy while efficiently handling concept drift.

data stream clustering  /  subspace learning  /  scalable subspace learning  /  concept drift detection
Hongwei YIN, Yuzhou NI, Wenjun HU. Scalable Subspace Learning for Clustering Data Streams[J]. Telecommunication Engineering, 2025 , 65 (11) : 1836 -1843 . DOI: 10.20079/j.issn.1001-893x.240618002
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Year 2025 volume 65 Issue 11
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Article Info
doi: 10.20079/j.issn.1001-893x.240618002
  • Receive Date：2024-06-18
  • Online Date：2026-04-15
  • Published：2025-11-28
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  • Received：2024-06-18
  • Revised：2024-08-05
Affiliations
    1School of Information Engineering，Huzhou University，Huzhou 31300，China
    2Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources, Huzhou 313000,China
    3Huzhou Key Laboratory of Aquatic Robot Technology，Huzhou 313000，China
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