Data density based clustering

Richard Hyde, Plamen Angelov

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

8 Citations (Scopus)

Abstract

A new, data density based approach to clustering is presented which automatically determines the number of clusters. By using RDE for each data sample the number of calculations is significantly reduced in offline mode and, further, the method is suitable for online use. The clusters allow a different diameter per feature/dimension creating hyper-ellipsoid clusters which are axis-orthogonal. This results in a greater differentiation between clusters where the clusters are highly asymmetrical. We illustrate this with 3 standard data sets, 1 artificial dataset and a large real dataset to demonstrate comparable results to Subtractive, Hierarchical, K-Means, ELM and DBScan clustering techniques. Unlike subtractive clustering we do not iteratively calculate P however. Unlike hierarchical we do not need O(N2) distances to be calculated and a cut-off threshold to be defined. Unlike k-means we do not need to predefine the number of clusters. Using the RDE equations to calculate the densities the algorithm is efficient, and requires no iteration to approach the optimal result. We compare the proposed algorithm to k-means, subtractive, hierarchical, ELM and DBScan clustering with respect to several criteria. The results demonstrate the validity of the proposed approach.

Original languageEnglish
Title of host publication2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479955381
DOIs
Publication statusPublished - 20 Oct 2014
Externally publishedYes
Event2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Bradford, United Kingdom
Duration: 8 Sep 201410 Sep 2014
http://www.computing.brad.ac.uk/ukci2014/

Workshop

Workshop2014 14th UK Workshop on Computational Intelligence, UKCI 2014
CountryUnited Kingdom
CityBradford
Period8/09/1410/09/14
Internet address

Keywords

  • big data
  • clustering
  • evolving clustering
  • incremental clustering

ASJC Scopus subject areas

  • Computational Theory and Mathematics

Cite this

Hyde, R., & Angelov, P. (2014). Data density based clustering. In 2014 14th UK Workshop on Computational Intelligence, UKCI 2014 - Proceedings [6930157] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UKCI.2014.6930157