Overlapping Clustering with Outliers Detection
Amira Rezgui, Chiheb-Eddine Ben N’Cir, Nadia Essoussi · 2014
Detecting overlapping groups is an important challenge in clustering offering relevant solutions for many applications domains. Recently, Parameterized R-OKM method was defined as an extension of OKM to control overlapping boundaries between clusters. However, the performance of both, OKM and Parameterized 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.