Overlapping Clustering with Outliers Detection

Amira Rezgui, Chiheb-Eddine Ben N'Cir, Nadia Essoussi


Detecting overlapping groups is an important challenge in clustering offering relevant solutions for many applications domains. Recently, Parametrized R-OKMmethod was defined as an extension of OKMto control overlapping boundaries between clusters. However, the performance of both, OKMand Parametrized R-OKM is considerably reduced when data contain outliers. The presence of outliers affects the resulting clusters and yields to clusters which do not fit the true structure of data. In order to improve the existing methods, we propose a robust method able to detect relevant overlapping clusters with outliers identification. Experiments performed on artificial and real multi-labeled data sets showed the effectiveness of the proposed method to produce relevant non disjoint groups.


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Paper Citation

in Harvard Style

Rezgui A., Ben N'Cir C. and Essoussi N. (2014). Overlapping Clustering with Outliers Detection . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 279-286. DOI: 10.5220/0004830002790286

in Bibtex Style

author={Amira Rezgui and Chiheb-Eddine Ben N'Cir and Nadia Essoussi},
title={Overlapping Clustering with Outliers Detection},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Overlapping Clustering with Outliers Detection
SN - 978-989-758-018-5
AU - Rezgui A.
AU - Ben N'Cir C.
AU - Essoussi N.
PY - 2014
SP - 279
EP - 286
DO - 10.5220/0004830002790286