时间：2025-01-08
本文（Online Sparse Representation Clustering for Evolving Data Streams）原载IEEE Transactions on Neural Networks and Learning Syetems，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 stream clustering can be performed to dis
cover the patterns underlying continuously arriving sequences
of data. A number of data stream clustering algorithms for
finding clusters in arbitrary shapes and handling outliers, such
as density-based clustering algorithms, have been proposed.
However, these algorithms are often limited in their ability to
construct and merge microclusters by measuring the Euclidean
distances between high-dimensional data objects, e.g., transfer
ring valuable knowledge from historical landmark windows to
the current landmark window, and exploiting evolving subspace
structures adaptively. We propose an online sparse representa
tion clustering (OSRC) method to learn an affinity matrix for
evaluating the relationships among high-dimensional data objects
in evolving data streams. We first introduce a low-dimensional
projection (LDP) into sparse representation to adaptively reduce
the potential negative influence associated with the noise and
redundancy contained in high-dimensional data. Then, we take
advantage of the l2,1-norm optimization technique to choose the
appropriate number of representative data objects and form
a specific dictionary for sparse representation. The specific
dictionary is integrated into sparse representation to adaptively
exploit the evolving subspace structures of the high-dimensional
data objects. Moreover, the data object representatives from
the current landmark window can transfer valuable knowledge
to the next landmark window. The experimental results based
on a synthetic dataset and six benchmark datasets validate the
effectiveness of the proposed method compared to that of state
of-the-art methods for data stream clustering.
Jie Chen, Shengxiang Yang, Conor Fahy, Zhu Wang, Yi-nan Guo, and Yingke Chen. Online Sparse Representation Clustering for Evolving Data Streams. IEEE Transactions on Neural Networks and Learning Syetems, Vol. 36, No. 1, January 2025.（论文下载）
