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GDPC: Gravitation-based Density Peaks Clustering algorithm

Author

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  • Jiang, Jianhua
  • Hao, Dehao
  • Chen, Yujun
  • Parmar, Milan
  • Li, Keqin

Abstract

The Density Peaks Clustering algorithm, which we refer to as DPC, is a novel and efficient density-based clustering approach, and it is published in Science in 2014. The DPC has advantages of discovering clusters with varying sizes and varying densities, but has some limitations of detecting the number of clusters and identifying anomalies. We develop an enhanced algorithm with an alternative decision graph based on gravitation theory and nearby distance to identify centroids and anomalies accurately. We apply our method to some UCI and synthetic data sets. We report comparative clustering performances using F-Measure and 2-dimensional vision. We also compare our method to other clustering algorithms, such as K-Means, Affinity Propagation (AP) and DPC. We present F-Measure scores and clustering accuracies of our GDPC algorithm compared to K-Means, AP and DPC on different data sets. We show that the GDPC has the superior performance in its capability of: (1) detecting the number of clusters obviously; (2) aggregating clusters with varying sizes, varying densities efficiently; (3) identifying anomalies accurately.

Suggested Citation

  • Jiang, Jianhua & Hao, Dehao & Chen, Yujun & Parmar, Milan & Li, Keqin, 2018. "GDPC: Gravitation-based Density Peaks Clustering algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 345-355.
    • Handle: RePEc:eee:phsmap:v:502:y:2018:i:c:p:345-355
    DOI: 10.1016/j.physa.2018.02.084
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    References listed on IDEAS

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    1. Alexander Strehl & Joydeep Ghosh, 2003. "Relationship-Based Clustering and Visualization for High-Dimensional Data Mining," INFORMS Journal on Computing, INFORMS, vol. 15(2), pages 208-230, May.
    2. Dong, Gaogao & Tian, Lixin & Du, Ruijin & Fu, Min & Stanley, H. Eugene, 2014. "Analysis of percolation behaviors of clustered networks with partial support–dependence relations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 370-378.
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    Cited by:

    1. Jiang, Jianhua & Chen, Yujun & Meng, Xianqiu & Wang, Limin & Li, Keqin, 2019. "A novel density peaks clustering algorithm based on k nearest neighbors for improving assignment process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 702-713.
    2. Jiang, Jianhua & Chen, Yujun & Hao, Dehao & Li, Keqin, 2019. "DPC-LG: Density peaks clustering based on logistic distribution and gravitation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 25-35.
    3. Yu, Hui & Chen, LuYuan & Yao, JingTao & Wang, XingNan, 2019. "A three-way clustering method based on an improved DBSCAN algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).

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