[1]HE Yulin,YANG Zhenyu,XIAO Youqi,et al.A new sparse representation-driven ensemble clustering algorithm for large-scale data[J].CAAI Transactions on Intelligent Systems,2026,21(4):888-907.[doi:10.11992/tis.202509003]
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A new sparse representation-driven ensemble clustering algorithm for large-scale data

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 4 Page number: 888-907 Column: 学术论文—机器学习 Public date: 2026-07-05
Title:
A new sparse representation-driven ensemble clustering algorithm for large-scale data
Author(s):
HE Yulin1; 2;  YANG Zhenyu1; 2;  XIAO Youqi1; 2;  HUANG Zhexue1; 2
1. Guangdong Laboratory of Artificial Intelligence and Digital Economy(Shenzhen), Shenzhen 518107, China;
2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Keywords:
large-scale clustering; sparse representation; ensemble clustering; representative point selection; distributed computing; Spark; large-scale data; consensus function
CLC:
TP391.4
DOI:
10.11992/tis.202509003
Abstract:
To address the challenges of inadequate sampling representativeness and high computational cost in large-scale high-dimensional data clustering, this paper proposes a sparse representation-driven ensemble clustering(SREC) algorithm. In the base clustering generation stage, SREC algorithm incorporates a hybrid sampling strategy that combines random sample partitioning with K-means, along with facebook AI similarity search(FAISS) indexing to build an efficient sparse graph. This approach effectively mitigates the limitation of local sampling in capturing the global data distribution. For the consensus function construction, a non-iterative weighted spectral consensus function is introduced, which employs the Tcut strategy to achieve high-precision cluster partitioning within the sparse graph. The SREC algorithm is implemented in a distributed environment using the Spark computing framework, and its performance is systematically evaluated on datasets containing over one million samples. Experimental results show that SREC outperforms ten selected mainstream clustering algorithms on the NMI, ARI, and ACC evaluation metrics, with improvements of 4.15%, 3.33% and 0.98%, respectively, over the second-best algorithm. Moreover, SREC algorithm exhibits strong stability, demonstrating low sensitivity to changes in the number of base clusters, as reflected by an NMI standard deviation of only 0.21%. Furthermore, when applied to clustering tasks with five million samples, SREC algorithm achieves a 51.8% increase in computational efficiency compared to the next-best algorithm, confirming its advantages for large-scale data clustering. The findings of this study can provide a reference for efficient clustering analysis of large-scale data, distributed data mining, and the design of related intelligent application systems.
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