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A potential-based clustering method with hierarchical optimization

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Abstract

This work proposes a novel data clustering algorithm based on the potential field model, with a hierarchical optimization mechanism on the algorithm. There are two stages in this algorithm. Firstly, we build an edge-weighted tree based on the mutual distances between all data points and their hypothetical potential values derived from the data distribution. Using the tree structure, the dataset can be divided into an appropriate number of initial sub-clusters, with the cluster centers close to the local minima of the potential field. Then the sub-clusters are further merged according to the well-designed merging criteria by analyzing their border potential values and the cluster average potential values. The proposed clustering algorithm follows a hierarchical clustering mechanism, and aims to optimize the initial sub-cluster results in the first stage. The algorithm takes advantage of the cluster merging criteria to merge the sub-clusters, so it can automatically stop the clustering process without designating the number of clusters in advance. The experimental results show that the proposed algorithm produces the most satisfactory clustering results in most cases compared with other existing methods, and can effectively identify the data clusters with arbitrary shape, size and density.

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China

    Xin Liu, Yongjian Liu, Qing Xie & Lin Li

  2. School of Computer Science and Technology, Soochow University, Soochow, China

    Zhixu Li

Authors
  1. Xin Liu
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  2. Yongjian Liu
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  3. Qing Xie
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  4. Lin Li
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  5. Zhixu Li
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Corresponding author

Correspondence to Qing Xie.

Additional information

This article belongs to the Topical Collection: Special Issue on Deep Mining Big Social Data

Guest Editors: Xiaofeng Zhu, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell

Part of the results in this work appeared in proceedings of the Big Data Partitioning and Mining Workshop associated with 2017 IEEE International Conference on Big Knowledge [11].

This research is partially supported by Natural Science Foundation of China (Grant No.61602353), National Social Science Foundation of China (Grant No.15BGL048) and the Fundamental Research Funds for the Central Universities (WUT:2017IVA053, WUT:2017IVB028 and WUT:2017II39GX).

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Liu, X., Liu, Y., Xie, Q. et al. A potential-based clustering method with hierarchical optimization. World Wide Web 21, 1617–1635 (2018). https://doi.org/10.1007/s11280-017-0509-2

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