A Distance-Type-Insensitive Clustering Approach

Xiaowei Gu, Plamen Parvanov Angelov, Zhijin Zhao

Research output: Contribution to journalArticlepeer-review

7 Citations (SciVal)
32 Downloads (Pure)


In this paper, we offer a method aiming to minimize the role of distance metric used in clustering. It is well known that distance metrics used in clustering algorithms heavily influence the end results and also make the algorithms sensitive to imbalanced attribute/feature scales. To solve these problems, a new clustering algorithm using a per-attribute/feature ranking operating mechanism is proposed in this paper. Ranking is a rarely used discrete, nonlinear operator by other clustering algorithms. However, it also has unique advantages over the dominantly used continuous operators. The proposed algorithm is based on the ranks of the data samples in terms of their spatial separation and is able to provide a more objective clustering result compared with the alternative approaches. Numerical examples on benchmark datasets prove the validity and effectiveness of the proposed concept and principles.
Original languageEnglish
Pages (from-to)622-634
Number of pages13
JournalApplied Soft Computing
Early online date31 Jan 2019
Publication statusPublished - 01 Apr 2019
Externally publishedYes


  • Clustering
  • Distance metric
  • Ranking
  • Spatial separation


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