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

详细信息    查看全文

• 作者：Renato Cordeiro de Amorim^a ; ^{r.amorim@herts.ac.uk" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Vladimir Makarenkov^b ; ^{makarenkov.vladimir@uqam.ca" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Clustering ; Mixed data ; Feature weighting ; K-Means ; Minkowski metric
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：15 January 2016
• 年：2016
• 卷：173
• 期：part_P3
• 页码：700-707
• 全文大小：389 K

文摘

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.