Collaborative clustering between different topological partitions
Antoine Lachaud, Nistor Grozavu, Basarab Mateï, Younès Bennani · 2017
The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple sites by applying different clustering algorithms and therefore improve the final clustering result. The purpose of this article is to introduce a new collaborative topological clustering approach based on Self-Organizing Maps (SOM) with more flexible structure. The main drawback of the previously proposed collaborative methods is they require a strong condition to carry out the collaboration, i.e. all the map related to each site must be learned with the same number of neurones. An alternative approach to overcome the problem of this dimensionality has been proposed in the case of k-means. The idea consists in modifying databases by adding virtual points which convey clustering information, to change the position of centers of the clustering solution. This approach seems promising because it is very flexible. In this paper we propose a method called VP2SOM (Virtual Points to SOM) which uses virtual points to realize the collaboration between SOM maps with different sizes. We have tested the proposed approach on several datasets and the early results seem promising.