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Spatial clustering with Density-Ordered tree

Author

Listed:
  • Cheng, Qing
  • Lu, Xin
  • Liu, Zhong
  • Huang, Jincai
  • Cheng, Guangquan

Abstract

Clustering has emerged as an active research direction for knowledge discovery in spatial databases. Most spatial clustering methods become ineffective when inappropriate parameters are given or when datasets of diverse shapes and densities are provided. To address this issue, we propose a novel clustering method, called SCDOT (Spatial Clustering with Density-Ordered Tree). By projecting a dataset to a Density-Ordered Tree, SCDOT partitions the data into several relatively small sub-clusters with a box-plot method. A heuristic method is proposed to find the genuine clusters by repeatedly merging sub-clusters and an iteration strategy is utilized to automatically determine input parameters. Moreover, we also provide an innovative way to identify cluster center and noise. Extensive experiments on both synthetic and real-world datasets demonstrate the superior performance of SCDOT over the baseline methods.

Suggested Citation

  • Cheng, Qing & Lu, Xin & Liu, Zhong & Huang, Jincai & Cheng, Guangquan, 2016. "Spatial clustering with Density-Ordered tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 188-200.
    • Handle: RePEc:eee:phsmap:v:460:y:2016:i:c:p:188-200
    DOI: 10.1016/j.physa.2016.05.041
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    References listed on IDEAS

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    1. Liu, Zhong & Huang, Jincai & Cheng, Guangquan, 2016. "Community detection in hypernetwork via Density-Ordered Tree partitionAuthor-Name: Cheng, Qing," Applied Mathematics and Computation, Elsevier, vol. 276(C), pages 384-393.
    2. Ralph Sundberg & Erik Melander, 2013. "Introducing the UCDP Georeferenced Event Dataset," Journal of Peace Research, Peace Research Institute Oslo, vol. 50(4), pages 523-532, July.
    3. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
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    Cited by:

    1. 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).
    2. Alokananda Dey & Siddhartha Bhattacharyya & Sandip Dey & Debanjan Konar & Jan Platos & Vaclav Snasel & Leo Mrsic & Pankaj Pal, 2023. "A Review of Quantum-Inspired Metaheuristic Algorithms for Automatic Clustering," Mathematics, MDPI, vol. 11(9), pages 1-44, April.

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