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A -Deviation Density Based Clustering Algorithm

Author

Listed:
  • Chen Jungan
  • Chen Jinyin
  • Yang Dongyong
  • Li Jun

Abstract

Due to the adoption of global parameters, DBSCAN fails to identify clusters with different and varied densities. To solve the problem, this paper extends DBSCAN by exploiting a new density definition and proposes a novel algorithm called -deviation density based DBSCAN (kDDBSCAN). Various datasets containing clusters with arbitrary shapes and different or varied densities are used to demonstrate the performance and investigate the feasibility and practicality of kDDBSCAN. The results show that kDDBSCAN performs better than DBSCAN.

Suggested Citation

  • Chen Jungan & Chen Jinyin & Yang Dongyong & Li Jun, 2018. "A -Deviation Density Based Clustering Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-16, February.
    • Handle: RePEc:hin:jnlmpe:3742048
    DOI: 10.1155/2018/3742048
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    Cited by:

    1. Stenfors, Alexis & Susai, Masayuki, 2021. "Spoofing and pinging in foreign exchange markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 70(C).

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