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Deep K-Means: A Simple and Effective Method for Data Clustering

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Neural Computing for Advanced Applications (NCAA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1265))

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Abstract

Clustering is one of the most fundamental techniques in statistic and machine learning. Due to the simplicity and efficiency, the most frequently used clustering method is the k-means algorithm. In the past decades, k-means and its various extensions have been proposed and successfully applied in data mining practical problems. However, previous clustering methods are typically designed in a single layer formulation. Thus the mapping between the low-dimensional representation obtained by these methods and the original data may contain rather complex hierarchical information. In this paper, a novel deep k-means model is proposed to learn such hidden representations with respect to different implicit lower-level characteristics. By utilizing the deep structure to conduct k-means hierarchically, the hierarchical semantics of data is learned in a layerwise way. The data points from same class are gathered closer layer by layer, which is beneficial for the subsequent learning task. Experiments on benchmark data sets are performed to illustrate the effectiveness of our method.

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Notes

  1. 1.

    For simplicity, the layer size (dimensionalities) of layer 1 to layer r is denoted as [\(k_1 \cdots k_r\)] in the experiments.

  2. 2.

    https://archive.ics.uci.edu/ml/datasets.html.

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Acknowledgments

This work was partially supported by the National Key Research and Development Program of China under Contract 2017YFB1002201, the National Natural Science Fund for Distinguished Young Scholar under Grant 61625204, the State Key Program of the National Science Foundation of China under Grant 61836006, and the Fundamental Research Funds for the Central Universities under Grant 1082204112364.

Author information

Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, 610065, China

    Shudong Huang

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Zhao Kang & Zenglin Xu

  3. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, China

    Zenglin Xu

  4. Center for Artificial Intelligence, Peng Cheng Lab, Shenzhen, 518055, China

    Zenglin Xu

Authors
  1. Shudong Huang
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  2. Zhao Kang
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  3. Zenglin Xu
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Corresponding author

Correspondence to Shudong Huang .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. Hefei University of Technology, Hefei, China

    Zhao Zhang

  3. Chongqing University, Chongqing, China

    Zhou Wu

  4. South China Normal University, Guangzhou, China

    Tianyong Hao

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Huang, S., Kang, Z., Xu, Z. (2020). Deep K-Means: A Simple and Effective Method for Data Clustering. In: Zhang, H., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2020. Communications in Computer and Information Science, vol 1265. Springer, Singapore. https://doi.org/10.1007/978-981-15-7670-6_23

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  • DOI: https://doi.org/10.1007/978-981-15-7670-6_23

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  • Online ISBN: 978-981-15-7670-6

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