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A fully autonomous data density based clustering algorithm

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A fully autonomous data density based clustering algorithm. / Hyde, Richard; Angelov, Plamen.

Evolving and Autonomous Learning Systems (EALS), 2014 IEEE Symposium on. Piscataway, N.J. : IEEE, 2014. p. 116-123.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Hyde, R & Angelov, P 2014, A fully autonomous data density based clustering algorithm. in Evolving and Autonomous Learning Systems (EALS), 2014 IEEE Symposium on. IEEE, Piscataway, N.J., pp. 116-123, IEEE, Orlando, United States, 9/12/14. https://doi.org/10.1109/EALS.2014.7009512

APA

Hyde, R., & Angelov, P. (2014). A fully autonomous data density based clustering algorithm. In Evolving and Autonomous Learning Systems (EALS), 2014 IEEE Symposium on (pp. 116-123). IEEE. https://doi.org/10.1109/EALS.2014.7009512

Vancouver

Hyde R, Angelov P. A fully autonomous data density based clustering algorithm. In Evolving and Autonomous Learning Systems (EALS), 2014 IEEE Symposium on. Piscataway, N.J.: IEEE. 2014. p. 116-123 https://doi.org/10.1109/EALS.2014.7009512

Author

Hyde, Richard ; Angelov, Plamen. / A fully autonomous data density based clustering algorithm. Evolving and Autonomous Learning Systems (EALS), 2014 IEEE Symposium on. Piscataway, N.J. : IEEE, 2014. pp. 116-123

Bibtex

@inproceedings{9968013ef0b24c44ab686688b08640d7,
title = "A fully autonomous data density based clustering algorithm",
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. A different radius per feature/ dimension creates hyper-ellipsoid clusters which are axis-orthogonal. This results in a greater differentiation between clusters where the clusters are highly asymmetrical. In this paper we update the DDC method to automatically derive suitable initial radii. The selection is data driven and requires no user input.We compare the performance of DDCAR with DDC and other standard clustering techniques by comparing the results across a selection of standard datasets and test datasets designed to test the abilities of the technique. By automatically estimating the initial radii we show that we can effectively cluster data with no user input. The results demonstrate the validity of the proposed approach as an autonomous, data driven clustering technique. We also demonstrate the speed and accuracy of the method on large datasets.",
keywords = "automated clustering, autonomous clustering, data density clustering, RDE, recursive density estimation",
author = "Richard Hyde and Plamen Angelov",
year = "2014",
month = dec
day = "9",
doi = "10.1109/EALS.2014.7009512",
language = "English",
isbn = "9781479944958 ",
pages = "116--123",
booktitle = "Evolving and Autonomous Learning Systems (EALS), 2014 IEEE Symposium on",
publisher = "IEEE",
note = "IEEE ; Conference date: 09-12-2014 Through 12-12-2014",

}

RIS

TY - GEN

T1 - A fully autonomous data density based clustering algorithm

AU - Hyde, Richard

AU - Angelov, Plamen

PY - 2014/12/9

Y1 - 2014/12/9

N2 - 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. A different radius per feature/ dimension creates hyper-ellipsoid clusters which are axis-orthogonal. This results in a greater differentiation between clusters where the clusters are highly asymmetrical. In this paper we update the DDC method to automatically derive suitable initial radii. The selection is data driven and requires no user input.We compare the performance of DDCAR with DDC and other standard clustering techniques by comparing the results across a selection of standard datasets and test datasets designed to test the abilities of the technique. By automatically estimating the initial radii we show that we can effectively cluster data with no user input. The results demonstrate the validity of the proposed approach as an autonomous, data driven clustering technique. We also demonstrate the speed and accuracy of the method on large datasets.

AB - 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. A different radius per feature/ dimension creates hyper-ellipsoid clusters which are axis-orthogonal. This results in a greater differentiation between clusters where the clusters are highly asymmetrical. In this paper we update the DDC method to automatically derive suitable initial radii. The selection is data driven and requires no user input.We compare the performance of DDCAR with DDC and other standard clustering techniques by comparing the results across a selection of standard datasets and test datasets designed to test the abilities of the technique. By automatically estimating the initial radii we show that we can effectively cluster data with no user input. The results demonstrate the validity of the proposed approach as an autonomous, data driven clustering technique. We also demonstrate the speed and accuracy of the method on large datasets.

KW - automated clustering

KW - autonomous clustering

KW - data density clustering

KW - RDE

KW - recursive density estimation

U2 - 10.1109/EALS.2014.7009512

DO - 10.1109/EALS.2014.7009512

M3 - Conference contribution/Paper

SN - 9781479944958

SP - 116

EP - 123

BT - Evolving and Autonomous Learning Systems (EALS), 2014 IEEE Symposium on

PB - IEEE

CY - Piscataway, N.J.

T2 - IEEE

Y2 - 9 December 2014 through 12 December 2014

ER -