Amorim, RC and Makarenkov, V (2016) Applying subclustering and Lp distance in Weighted K-Means with distributed centroids. Neurocomputing, 173 (P3). pp. 700-707. DOI https://doi.org/10.1016/j.neucom.2015.08.018
Amorim, RC and Makarenkov, V (2016) Applying subclustering and Lp distance in Weighted K-Means with distributed centroids. Neurocomputing, 173 (P3). pp. 700-707. DOI https://doi.org/10.1016/j.neucom.2015.08.018
Amorim, RC and Makarenkov, V (2016) Applying subclustering and Lp distance in Weighted K-Means with distributed centroids. Neurocomputing, 173 (P3). pp. 700-707. DOI https://doi.org/10.1016/j.neucom.2015.08.018
Abstract
We consider the Weighted K-Means algorithm with distributed centroids aimed at clustering data sets with numerical, categorical and mixed types of data. Our approach allows given features (i.e., variables) to have different weights at different clusters. Thus, it supports the intuitive idea that features may have different degrees of relevance at different clusters. We use the Minkowski metric in a way that feature weights become feature re-scaling factors for any considered exponent. Moreover, the traditional Silhouette clustering validity index was adapted to deal with both numerical and categorical types of features. Finally, we show that our new method usually outperforms traditional K-Means as well as the recently proposed WK-DC clustering algorithm.
Item Type: | Article |
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Uncontrolled Keywords: | Clustering Mixed data Feature weighting K-Means Minkowski metric |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 18 Sep 2017 12:44 |
Last Modified: | 30 Oct 2024 19:36 |
URI: | http://repository.essex.ac.uk/id/eprint/20361 |
Available files
Filename: MWk_Prototype.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0