A fully autonomous Data Density based Clustering technique

Richard Hyde; Plamen Angelov

doi:10.1109/EALS.2014.7009512

A fully autonomous Data Density based Clustering technique

Richard Hyde, Plamen Angelov

Lancaster University

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

9 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 language	English
Title of host publication	IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014
Subtitle of host publication	2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	116-123
Number of pages	8
ISBN (Electronic)	9781479944958, 9781479944941
DOIs	https://doi.org/10.1109/EALS.2014.7009512
Publication status	Published - 15 Jan 2015
Externally published	Yes
Event	2014 IEEE Symposium on Evolving and Autonomous Learning Systems - Orlando, United States Duration: 9 Dec 2014 → 12 Dec 2014 https://www.caos.inf.uc3m.es/eals14/

Conference

Conference	2014 IEEE Symposium on Evolving and Autonomous Learning Systems
Abbreviated title	EALS 2014
Country/Territory	United States
City	Orlando
Period	9/12/14 → 12/12/14
Internet address	https://www.caos.inf.uc3m.es/eals14/

Keywords

Automated clustering
Autonomous clustering
Data density
RDE
Recursive density estimation

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

10.1109/EALS.2014.7009512

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). Article 7009512 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EALS.2014.7009512

A fully autonomous Data Density based Clustering technique. / Hyde, Richard; Angelov, Plamen.
IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 116-123 7009512.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

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., 7009512, Institute of Electrical and Electronics Engineers Inc., pp. 116-123, 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Orlando, Florida, United States, 9/12/14. https://doi.org/10.1109/EALS.2014.7009512

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A fully autonomous Data Density based Clustering technique

Abstract

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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