Name
Abstract | ||
|---|---|---|
The aim of collaborative clustering is to reveal the common structure of data distributed on different sites. In this paper, we present a new approach for the topological collaborative clustering using a generative model, which is the Generative Topographic Mappings (GTM). In this case, maps representing different sites could collaborate without recourse to the original data, preserving their privacy. Depending ont the data structure, there are three different ways of collaborative clustering: horizontal, vertical and hybrid. In this study we introduce the Collaborative GTM for the vertical collaboration. The article 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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-34481-7_72 | ICONIP |
Keywords | Field | DocType |
original data,different way,collaborative clustering,common structure,collaborative gtm,topological collaborative,collaborative generative topographic mapping,new approach,different site,data structure | Data mining,Data structure,Topographic map,Computer science,Generative topographic mapping,Artificial intelligence,Formalism (philosophy),Generative grammar,Cluster analysis,Machine learning,Generative model | Conference |
Volume | ISSN | Citations |
7664 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamad Ghassany | 1 | 19 | 1.87 |
| Nistor Grozavu | 2 | 67 | 16.76 |
| Younès Bennani | 3 | 269 | 53.18 |