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Initialization-similarity clustering algorithm

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Abstract

Classic k-means clustering algorithm randomly selects centroids for initialization to possibly output unstable clustering results. Moreover, random initialization makes the clustering result hard to reproduce. Spectral clustering algorithm is a two-step strategy, which first generates a similarity matrix and then conducts eigenvalue decomposition on the Laplacian matrix of the similarity matrix to obtain the spectral representation. However, the goal of the first step in the spectral clustering algorithm does not guarantee the best clustering result. To address the above issues, this paper proposes an Initialization-Similarity (IS) algorithm which learns the similarity matrix and the new representation in a unified way and fixes initialization using the sum-of-norms regularization to make the clustering more robust. The experimental results on ten real-world benchmark datasets demonstrate that our IS clustering algorithm outperforms the comparison clustering algorithms in terms of three evaluation metrics for clustering algorithm including accuracy (ACC), normalized mutual information (NMI), and Purity.

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Funding

This work was partially supported by the Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (MIMS18-M-01), the Natural Science Foundation of China (Grants No: 61876046 and 61573270); the Guangxi High Institutions Program of Introducing 100 High-Level Overseas Talents; the Strategic Research Excellence Fund at Massey University, and the Marsden Fund of New Zealand (Grant No: MAU1721).

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Authors and Affiliations

  1. GuangXi Key Lab of Multi-Source Information Mining and Security, GuangXi Normal University, Guilin, 541004, China

    Tong Liu & Xiaofeng Zhu

  2. School of Natural & Computational Sciences, Massey University, Auckland, New Zealand

    Tong Liu, Jingting Zhu, Jukai Zhou & Xiaofeng Zhu

  3. Hebei GEO University, Shijiazhuang, 050000, China

    YongXin Zhu

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  1. Tong Liu
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  5. Xiaofeng Zhu
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Correspondence to Xiaofeng Zhu.

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Liu, T., Zhu, J., Zhou, J. et al. Initialization-similarity clustering algorithm. Multimed Tools Appl 78, 33279–33296 (2019). https://doi.org/10.1007/s11042-019-7663-8

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