A fully autonomous Data Density based Clustering technique

Richard Hyde, Plamen Angelov

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

7 Citations (Scopus)

Abstract

A recently introduced data density based approach to clustering, known as Data Density based Clustering has been presented which automatically determines the number of clusters. By using the Recursive Density Estimation for each point the number of calculations is significantly reduced in offline mode and, further, the method is suitable for online use. The Data Density based Clustering method however requires an initial cluster radius to be entered.

Original languageEnglish
Title of host publicationIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014
Subtitle of host publication2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages116-123
Number of pages8
ISBN (Electronic)9781479944958, 9781479944941
DOIs
Publication statusPublished - 15 Jan 2015
Externally publishedYes
Event2014 IEEE Symposium on Evolving and Autonomous Learning Systems - Orlando, United States
Duration: 9 Dec 201412 Dec 2014
https://www.caos.inf.uc3m.es/eals14/

Conference

Conference2014 IEEE Symposium on Evolving and Autonomous Learning Systems
Abbreviated titleEALS 2014
CountryUnited States
CityOrlando
Period9/12/1412/12/14
Internet address

Keywords

  • Automated clustering
  • Autonomous clustering
  • Data density
  • RDE
  • Recursive density estimation

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Hyde, R., & Angelov, P. (2015). A fully autonomous Data Density based Clustering technique. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings (pp. 116-123). [7009512] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EALS.2014.7009512