From horizontal to vertical collaborative clustering using generative topographic maps

Article type: Research Article

Authors: Sublime, Jérémie^{a; *} | Grozavu, Nistor^b | Cabanes, Guénaël^b | Bennani, Younès^b | Cornuéjols, Antoine^a

Affiliations: [a] AgroParisTech, INRA UMR MIA 518, Paris, France | [b] LIPN UMR CNRS 7030, Villetaneuse, France

Correspondence: [*] Corresponding author: Jérémie Sublime, AgroParisTech, INRA UMR MIA 518, 16 rue Claude Bernard, 75231 Paris, France. E-mail:jeremie.sublime@agroparistech.fr

Abstract: Collaborative clustering is a recent field of Machine Learning that shows similarities with both ensemble learning and transfer learning. Using a two-step approach where different clustering algorithms first process data individually and then exchange their information and results with a goal of mutual improvement, collaborative clustering has shown promising performances when trying to have several algorithms working on the same data. However the field is still lagging behind when it comes to transfer learning where several algorithms are working on different data with similar clusters and the same features. In this article, we propose an original method where we combine the topological structure of the Generative Topographic Mapping (GTM) algorithm and take advantage of it to transfer information between collaborating algorithms working on different data sets featuring similar distributions. The proposed approach has been validated on several data sets, and the experimental results have shown very promising performances.

Keywords: Collaborative clustering, ensemble learning, knowledge transfer

DOI: 10.3233/HIS-160219

Journal: International Journal of Hybrid Intelligent Systems, vol. 12, no. 4, pp. 245-256, 2015