Title :
Vertical collaborative clustering using generative topographic maps
Author :
J?r?mie Sublime;Nistor Grozavu;Younes Bennani;Antoine Cornuejols
Author_Institution :
AgroParisTech, INRA UMR MIA 518 Paris, 16 rue Claude Bernard, 75231 Paris, France
Abstract :
Collaborative clustering is a recent field of Machine Learning that shows similarities with both transfer learning and ensemble learning. It uses two-step approaches where different clustering algorithms first process data individually and then exchange their information and results with a goal of mutual improvement. In this article, we introduce a new collaborative learning approach based on collaborative clustering principles and applied to the Generative Topographic Mapping (GTM) algorithm. Our method consists in applying the GTM algorithm on different data sets where similar clusters can be found (same feature spaces and similar data distributions), and then to use a collaborative framework on the generated maps with the goal of transferring knowledge between them. The proposed approach has been validated on several data sets, and the experimental results have shown very promising performances.
Keywords :
"Clustering algorithms","Collaboration","Prototypes","Mathematical model","Neurons","Data models","Phase change materials"
Conference_Titel :
Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of
DOI :
10.1109/SOCPAR.2015.7492807