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A new type of distance metric and its use for clustering

Gu, Xiaowei, Angelov, Plamen P., Kangin, Dmitry, Principe, Jose C. (2017) A new type of distance metric and its use for clustering. Evolving Systems, 8 (3). pp. 167-177. ISSN 1868-6478. (doi:10.1007/s12530-017-9195-7) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90209)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1007/s12530-017-9195-7

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.

Item Type: Article
DOI/Identification number: 10.1007/s12530-017-9195-7
Uncontrolled keywords: Cosine similarity; Distance metric; Metric space; Clustering; High dimensional data streams processing
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 14 Sep 2021 13:28 UTC
Last Modified: 15 Sep 2021 14:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90209 (The current URI for this page, for reference purposes)
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