Authors:
Chiheb-Eddine Ben N'Cir
1
;
Nadia Essoussi
1
and
Patrice Bertrand
2
Affiliations:
1
University of Tunis, Tunisia
;
2
Université Paris-Dauphine, France
Keyword(s):
Overlapping clustering, Overlapping K-Means, Kernel Methods, Gram matrix, Kernel induced distance measure.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
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.