A new type of distance metric and its use for clustering

Xiaowei Gu, Plamen Parvanov Angelov, Dmitry Kangin, Jose Principe

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

In order to address high dimensional problems, a new ‘direction-aware’ metric is introduced in this paper. This new distance is a combination of two components: (1) the traditional Euclidean distance and (2) an angular/directional divergence, derived from the cosine similarity. The newly introduced metric combines the advantages of the Euclidean metric and cosine similarity, and is defined over the Euclidean space domain. Thus, it is able to take the advantage from both spaces, while preserving the Euclidean space domain. The direction-aware distance has wide range of applicability and can be used as an alternative distance measure for various traditional clustering approaches to enhance their ability of handling high dimensional problems. A new evolving clustering algorithm using the proposed distance is also proposed in this paper. Numerical examples with benchmark datasets reveal that the direction-aware distance can effectively improve the clustering quality of the k-means algorithm for high dimensional problems and demonstrate the proposed evolving clustering algorithm to be an effective tool for high dimensional data streams processing.

Original languageEnglish
Pages (from-to)167-177
Number of pages11
JournalEvolving Systems
Volume8
Issue number3
Early online date26 Jul 2017
DOIs
Publication statusPublished - 01 Sept 2017
Externally publishedYes

Keywords

  • Clustering
  • Cosine similarity
  • Distance metric
  • High dimensional data streams processing
  • Metric space

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