Name
Abstract | ||
|---|---|---|
The aim of collaborative clustering is to reveal the common structure of data which are distributed on different sites. The topological collaborative clustering, based on Self-Organizing Maps (SOM) is an unsupervised learning method which is able to use the output of other SOMs from other sites during the learning. This paper investigates the impact of the diversity between collaborators on the collaboration's quality and presents a study of different diversity indexes for collaborative clustering. Based on experiments on artificial and real datasets, we demonstrated that the quality and the diversity of the collaboration can have an important impact on the quality of the collaboration and that not all diversity indexes are relevant for this task. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IJCNN.2014.6889528 | Neural Networks |
Keywords | Field | DocType |
data mining,pattern clustering,self-organising feature maps,unsupervised learning,SOM,artificial datasets,collaboration quality,collaborator diversity,data mining,diversity analysis,diversity index,real datasets,self-organizing maps,topological collaborative clustering,unsupervised learning method | Data mining,Computer science,Consensus clustering,Unsupervised learning,Artificial intelligence,Conceptual clustering,Cluster analysis,Machine learning,Diversity analysis | Conference |
ISSN | Citations | PageRank |
2161-4393 | 3 | 0.40 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nistor Grozavu | 1 | 67 | 16.76 |
| Guenael Cabanes | 2 | 5 | 1.11 |
| Younès Bennani | 3 | 269 | 53.18 |