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 formalism of topological collaborative clustering using prototype-based clustering techniques; in particular we formulate our approach using Kohonen's Self-Organizing Maps. Maps representing different sites could collaborate without recourse to the original data, preserving their privacy. We present two different approaches of collaborative clustering: horizontal and vertical. The strength of collaboration (confidence exchange) between each pair of datasets is determined by a parameter, we call coefficient of collaboration, to be estimated iteratively during the collaboration phase using a gradient-based optimization, for both the approaches. The proposed approaches have been validated on several datasets and experimental results have shown very promising performance. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1142/S1469026812500174 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS |
Keywords | Field | DocType |
Collaborative clustering, distributed data, prototype-based clustering, self-organizing maps, privacy preserving | Data mining,CURE data clustering algorithm,Clustering high-dimensional data,Collaborative filtering,Correlation clustering,Computer science,Self-organizing map,Artificial intelligence,Conceptual clustering,Cluster analysis,Brown clustering,Machine learning | Journal |
Volume | Issue | ISSN |
11 | 3 | 1469-0268 |
Citations | PageRank | References |
10 | 0.67 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohamad Ghassany | 1 | 19 | 1.87 |
| Nistor Grozavu | 2 | 67 | 16.76 |
| Younès Bennani | 3 | 269 | 53.18 |