Collaborative multi-view clustering

Mohamad Ghassany, Nistor Grozavu, Younès Bennani

Abstract: The purpose of this article is to introduce a new collaborative multi-view clustering approach based on a probabilistic model. The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple data sites by applying clustering techniques. The strength of the collaboration between each pair of data repositories is determined by a fixed parameter. Previous works considered deterministic techniques such as Fuzzy C-Means (FCM) and Self-Organizing Maps (SOM). In this paper, we present a new approach for the collaborative clustering using a generative model, which is the Generative Topographic Mappings (GTM). Maps representing different sites could collaborate without recourse to the original data, preserving their privacy. We present the approach for multi-view collaboration using GTM, where data sets have the same observations but presented in different feature space; i.e. different dimensions. The proposed approach has been validated on several data sets, and experimental results have shown very promising performance.