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Synchronization-based scalable subspace clustering of high-dimensional data

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Abstract

How to address the challenges of the “curse of dimensionality” and “scalability” in clustering simultaneously? In this paper, we propose arbitrarily oriented synchronized clusters (ORSC), a novel effective and efficient method for subspace clustering inspired by synchronization. Synchronization is a basic phenomenon prevalent in nature, capable of controlling even highly complex processes such as opinion formation in a group. Control of complex processes is achieved by simple operations based on interactions between objects. Relying on the weighted interaction model and iterative dynamic clustering, our approach ORSC (a) naturally detects correlation clusters in arbitrarily oriented subspaces, including arbitrarily shaped nonlinear correlation clusters. Our approach is (b) robust against noise and outliers. In contrast to previous methods, ORSC is (c) easy to parameterize, since there is no need to specify the subspace dimensionality or other difficult parameters. Instead, all interesting subspaces are detected in a fully automatic way. Finally, (d) ORSC outperforms most comparison methods in terms of runtime efficiency and is highly scalable to large and high-dimensional data sets. Extensive experiments have demonstrated the effectiveness and efficiency of our approach.

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Acknowledgements

The research was supported partially by the National Natural Science Foundation of China (Grant Nos. 61403062, 61433014, 41601025), China Postdoctoral Science Foundation (2014M552344, 2015M580786), Science-Technology Foundation for Young Scientist of SiChuan Province (2016JQ0007) and Fundamental Research Funds for the Central Universities (Grant Nos. ZYGX2014J053, ZYGX2014J091).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Junming Shao, Xinzuo Wang & Qinli Yang

  2. Institute for Computer Science, University of Vienna, 1090, Vienna, Austria

    Claudia Plant

  3. Institute for Computer Science, University of Munich, 80538, Munich, Germany

    Christian Böhm

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  1. Junming Shao
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  2. Xinzuo Wang
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  3. Qinli Yang
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  4. Claudia Plant
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  5. Christian Böhm
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Correspondence to Junming Shao.

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Shao, J., Wang, X., Yang, Q. et al. Synchronization-based scalable subspace clustering of high-dimensional data. Knowl Inf Syst 52, 83–111 (2017). https://doi.org/10.1007/s10115-016-1013-1

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