Name
Abstract | ||
|---|---|---|
The aim of collaborative clustering is to reveal the common underlying structure of data spread across multiple data sites by applying clustering techniques. The idea of Collaborative Clustering is that each collaborator share some information about the segmentation (structure) of its local data and improve its own clustering with the information provided by the other collaborators. This paper analyses the impact of the Quality of the potential Collaborators to the quality of the collaboration for a Topological Collaborative Clustering Algorithm based on the learning of a Self-Organizing Map. Experimental analysis on four real vector data-sets showed that the diversity between collaborators impact the quality of the collaboration. We also showed that the internal indexes of quality are a good estimator of the increase of quality due to the collaboration. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/SSCI.2015.117 | 2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI) |
Keywords | Field | DocType |
distributed databases,collaboration,prototypes,indexes,clustering algorithms | Data mining,Multiple data,Information retrieval,Segmentation,Computer science,Distributed database,Conceptual clustering,Cluster analysis,Estimator | Conference |
Citations | PageRank | References |
2 | 0.37 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| parisa rastin | 1 | 4 | 0.78 |
| Guenael Cabanes | 2 | 5 | 1.11 |
| Nistor Grozavu | 3 | 67 | 16.76 |
| Younes Bennani | 4 | 2 | 0.71 |