Learning confidence exchange in Collaborative Clustering

Nistor Grozavu, Mohamad Ghassany, Younès Bennani

Abstract: The aim of collaborative clustering is to reveal the common structure of data which are distributed on different sites. The topological collaborative clustering (based on Kohonen Self-Organizing Maps) allows to take into account other maps without recourse to the data in an unsupervised learning. In this paper, the approach is presented in the case of SOM and it is valid for all prototypes based classifications methods. The strength of the collaboration between each pair of datasets is determined by a fixed parameter for the both, vertical and horizontal topological collaborative clustering. In this study, learning the confidence exchange is presented for the both topological collaborative clustering approaches by using the topological knowledge. The gradient based optimization is used to set the value of the confidence parameter for each collaboration. The paper presents the formalism of the approach and its validation. The proposed approach has been validated on several datasets and experimental results have shown very promising performance.