Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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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 -