Article: Constrains optimal propagation-based modified semi-supervised spectral clustering for large-scale data Journal: International Journal of Applied Decision Sciences (IJADS) 2018 Vol.11 No.3 pp.256 - 273 Abstract: We focus on the problem of high computational complexity in the clustering process of traditional spectral clustering algorithm that cannot satisfy the requirement of current large-scale data clustering applications. In this article, we establish a constrained optimal propagation based semi-supervised large-scale data clustering model. In this model, micro similarity matrix is constructed by using prior dotted pair constraint information at first. On this basis, the <i>Gabow</i> algorithm is exploited to extract each strongly connected component from the micro similarity matrix that is represented by its connected graph. Then, a new constrained optimisation propagation algorithm for each strongly connected component is proposed to calculate the similarity of the whole dataset. Finally, we employ the singular value decomposition and the accelerated <i>k-means</i> algorithm to obtain the clustering results of large-scale data. Experiments on multiple standard testing datasets show that compared with other previous research results in this field, the proposed clustering model has higher clustering accuracy and lower computation complexity, and is more suitable for large-scale data clustering applications. Inderscience Publishers - linking academia, business and industry through research

Title: Constrains optimal propagation-based modified semi-supervised spectral clustering for large-scale data

Authors: Dayu Xu; Xuyao Zhang; Jiaqi Huang; Hailin Feng

Addresses: School of Information Engineering, Zhejiang A&F University, Hangzhou, Zhejiang, China; Sunyard System Engineering Co., Ltd., Hangzhou, Zhejiang, China ' School of Information Engineering, Zhejiang A&F University, Hangzhou, Zhejiang, China ' School of Information Engineering, Zhejiang A&F University, Hangzhou, Zhejiang, China ' School of Information Engineering, Zhejiang A&F University, Hangzhou, Zhejiang, China

Abstract: We focus on the problem of high computational complexity in the clustering process of traditional spectral clustering algorithm that cannot satisfy the requirement of current large-scale data clustering applications. In this article, we establish a constrained optimal propagation based semi-supervised large-scale data clustering model. In this model, micro similarity matrix is constructed by using prior dotted pair constraint information at first. On this basis, the Gabow algorithm is exploited to extract each strongly connected component from the micro similarity matrix that is represented by its connected graph. Then, a new constrained optimisation propagation algorithm for each strongly connected component is proposed to calculate the similarity of the whole dataset. Finally, we employ the singular value decomposition and the accelerated k-means algorithm to obtain the clustering results of large-scale data. Experiments on multiple standard testing datasets show that compared with other previous research results in this field, the proposed clustering model has higher clustering accuracy and lower computation complexity, and is more suitable for large-scale data clustering applications.

Keywords: spectral clustering; large-scale data; pairwise constraint; affinity propagation; singular value decomposition; SVD.

DOI: 10.1504/IJADS.2018.092793

International Journal of Applied Decision Sciences, 2018 Vol.11 No.3, pp.256 - 273

Received: 14 Aug 2017
Accepted: 07 Nov 2017
Published online: 29 Jun 2018 *

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Title: Constrains optimal propagation-based modified semi-supervised spectral clustering for large-scale data

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