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Merging DBSCAN and Density Peak for Robust Clustering

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Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series (ICANN 2019)

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Abstract

In data clustering, density based algorithms are well known for the ability of detecting clusters of arbitrary shapes. DBSCAN is a widely used density based clustering approach, and the recently proposed density peak algorithm has shown significant potential in experiments. However, the DBSCAN algorithm may misclassify border data points of small density as noises and does not work well with large density variance across clusters, and the density peak algorithm has a large dependence on the detected cluster centers. To circumvent these problems, we make a study of these two algorithms and find that they have some complementary properties. We then propose to combine these two algorithms to overcome their problems. Specifically, we use the DP algorithm to detect cluster centers and then determine the parameters for DBSCAN adaptively. After DBSCAN clustering, we further use the DP algorithm to include border data points of small density into clusters. By combining the complementary properties of these two algorithms, we manage to relieve the problems of DBSCAN and avoid the drawbacks of the density peak algorithm in the meanwhile. Our algorithm is tested with synthetic and real datasets, and is demonstrated to perform better than DBSCAN and density peak algorithms, as well as some other clustering algorithms.

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Acknowledgement

This work is supported in part by the National Natural Science Foundation of China under Grant No. 61473045, and by the Natural Science Foundation of Liaoning Province under Grant No. 20170540013.

Author information

Authors and Affiliations

  1. College of Engineering, Bohai University, Jinzhou, 121013, China

    Jian Hou, Chengcong Lv & Aihua Zhang

  2. College of Information Sciences, Bohai University, Jinzhou, 121013, China

    Xu E

Authors
  1. Jian Hou
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  2. Chengcong Lv
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  3. Aihua Zhang
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  4. Xu E
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Corresponding author

Correspondence to Jian Hou .

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Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Hou, J., Lv, C., Zhang, A., E, X. (2019). Merging DBSCAN and Density Peak for Robust Clustering. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_48

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  • DOI: https://doi.org/10.1007/978-3-030-30490-4_48

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-30490-4

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