On the Extension of k-Means for Overlapping Clustering - Average or Sum of Clusters’ Representatives?

Chiheb-Eddine Ben N'Cir, Nadia Essoussi

2013

Abstract

Clustering is an unsupervised learning technique which aims to fit structures for unlabeled data sets. Identifying non disjoint groups is an important issue in clustering. This issue arises naturally because many real life applications need to assign each observation to one or several clusters. To deal with this problem, recent proposed methods are based on theoretical, rather than heuristic, model and introduce overlaps in their optimized criteria. In order to model overlaps between clusters, some of these methods use the average of clusters’ prototypes while other methods are based on the sum of clusters’ prototypes. The use of SUM or AVERAGE can have significant impact on the theoretical validity of the method and affects induced patterns. Therefore, we study in this paper patterns induced by these approaches through the comparison of patterns induced by Overlapping k-means (OKM) and Alternating Least Square (ALS) methods which generalize k-means for overlapping clustering and are based on AVERAGE and SUM approaches respectively.

Paper Citation

in Harvard Style

Ben N'Cir C. and Essoussi N. (2013). On the Extension of k-Means for Overlapping Clustering - Average or Sum of Clusters’ Representatives? . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013) ISBN 978-989-8565-75-4, pages 208-213. DOI: 10.5220/0004626502080213

in Bibtex Style

@conference{kdir13,
author={Chiheb-Eddine Ben N'Cir and Nadia Essoussi},
title={On the Extension of k-Means for Overlapping Clustering - Average or Sum of Clusters’ Representatives?},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013)},
year={2013},
pages={208-213},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004626502080213},
isbn={978-989-8565-75-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013)
TI - On the Extension of k-Means for Overlapping Clustering - Average or Sum of Clusters’ Representatives?
SN - 978-989-8565-75-4
AU - Ben N'Cir C.
AU - Essoussi N.
PY - 2013
SP - 208
EP - 213
DO - 10.5220/0004626502080213