FREDPC: A Feasible Residual Error-Based Density Peak Clustering Algorithm With the Fragment Merging Strategy

Milan D Parmar, Wei Pang, Dehao Hao, Jianhua Jang, Wang Liupu, You Zhou (Corresponding Author)

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3 Citations (Scopus)
2 Downloads (Pure)


The most common issues for many clustering algorithms include the slow convergence, requirement for pre-specification of a number of parameters, and the lack of robustness when dealing with anomalies. Recently, the density peak clustering (DPC) algorithm was proposed to discover the centers of clusters by finding the density peaks in a dataset based on their local densities. The DPC needs neither an iterative process nor a large number of parameters, and it supports a heuristic approach, known as the decision graph, to manually select cluster centroids. However, the selection of the key parameters of the DPC was not systematically investigated. In this paper, we propose the feasible residual error-based density peak clustering algorithm with the fragment merging strategy, where the local density within the neighborhood region is measured through the residual error computation and the resulting residual errors are then used to generate residual fragments for cluster formation. The model parameters are then able to be calculated from the equations with statistical theoretical justification. We also develop a semi-automatic cluster identification method to eliminate the iterative process of manual centroid selection. The robustness and effectiveness of the proposed algorithm compared to the DPC and other clustering algorithms are demonstrated through experiments on standard benchmark datasets. The proposed method named feasible residual error-based density peak clustering (FREDPC) algorithm with the fragment merging strategy only needs to perform in one single step without any iteration and thus it is fast and has a great potential to be applied on a wide range of applications.
Original languageEnglish
Pages (from-to)89789-89804
Number of pages16
JournalIEEE Access
Publication statusPublished - 3 Jul 2019



  • clustering
  • density peak clustering
  • anomaly detection
  • residual error
  • residual fragment
  • Clustering

ASJC Scopus subject areas

  • Engineering(all)
  • Materials Science(all)
  • Computer Science(all)

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