KERNEL OVERLAPPING K-MEANS FOR CLUSTERING IN FEATURE SPACE

Chiheb-Eddine Ben N'Cir, Nadia Essoussi, Patrice Bertrand

Abstract

Producing overlapping schemes is a major issue in clustering. Recent overlapping methods rely on the search of optimal clusters and are based on different metrics, such as Euclidean distance and I-Divergence, used to measure closeness between observations. In this paper, we propose the use of kernel methods to look for separation between clusters in a high feature space. For detecting non linearly separable clusters, we propose a Kernel Overlapping k-Means algorithm (KOKM) in which we use kernel induced distance measure. The number of overlapping clusters is estimated using the Gram matrix. Experiments on different datasets show the correctness of the estimation of number of clusters and show that KOKM gives better results when compared to overlapping k-means.

References

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Paper Citation


in Harvard Style

Ben N'Cir C., Essoussi N. and Bertrand P. (2010). KERNEL OVERLAPPING K-MEANS FOR CLUSTERING IN FEATURE SPACE . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 250-256. DOI: 10.5220/0003095102500256


in Bibtex Style

@conference{kdir10,
author={Chiheb-Eddine Ben N'Cir and Nadia Essoussi and Patrice Bertrand},
title={KERNEL OVERLAPPING K-MEANS FOR CLUSTERING IN FEATURE SPACE},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},
year={2010},
pages={250-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003095102500256},
isbn={978-989-8425-28-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
TI - KERNEL OVERLAPPING K-MEANS FOR CLUSTERING IN FEATURE SPACE
SN - 978-989-8425-28-7
AU - Ben N'Cir C.
AU - Essoussi N.
AU - Bertrand P.
PY - 2010
SP - 250
EP - 256
DO - 10.5220/0003095102500256