Applying subclustering and Lp distance in Weighted K-Means with distributed centroids

Cordeiro De Amorim, Renato and Makarenkov, Vladimir (2016) Applying subclustering and Lp distance in Weighted K-Means with distributed centroids. Neurocomputing, 173 (3). pp. 700-707. ISSN 0925-2312
Copy

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.


picture_as_pdf
MWk_Prototype.pdf
subject
Submitted Version
Available under Creative Commons: BY-NC-ND 4.0

View Download

Atom BibTeX OpenURL ContextObject in Span OpenURL ContextObject Dublin Core MPEG-21 DIDL Data Cite XML EndNote HTML Citation METS MODS RIOXX2 XML Reference Manager Refer ASCII Citation
Export

Downloads