  Overlapping clustering problem is an important learning issue in which
clusters are not mutually exclusive and each object may belongs simultaneously
to several clusters. This paper presents a kernel based method that produces
overlapping clusters on a high feature space using mercer kernel techniques to
improve separability of input patterns. The proposed method, called
OKM-K(Overlapping $k$-means based kernel method), extends OKM (Overlapping
$k$-means) method to produce overlapping schemes. Experiments are performed on
overlapping dataset and empirical results obtained with OKM-K outperform
results obtained with OKM.
