Article Dans Une Revue Journal of Artificial Intelligence and Soft Computing Research Année : 2019

Impact of Learners’ Quality and Diversity in Collaborative Clustering

Résumé

Collaborative Clustering is a data mining task the aim of which is to use several clustering algorithms to analyze different aspects of the same data. 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 shares some information about the segmentation (structure) of its local data and improve its own clustering with the information provided by the other learners. This paper analyses the impact of the quality and the diversity of the potential learners to the quality of the collaboration for topological collaborative clustering algorithms based on the learning of a Self-Organizing Map (SOM). Experimental analysis on real data-sets showed that the diversity between learners impact the quality of the collaboration. We also showed that some internal indexes of quality are a good estimator of the increase of quality due to the collaboration.
Fichier principal
Vignette du fichier
10.2478_jaiscr-2018-0030.pdf (1.26 Mo) Télécharger le fichier
Origine Publication financée par une institution
licence

Dates et versions

hal-02994813 , version 1 (13-01-2025)

Licence

Identifiants

Citer

Parisa Rastin, Basarab Matei, Guénaël Cabanes, Nistor Grozavu, Younès Bennani. Impact of Learners’ Quality and Diversity in Collaborative Clustering. Journal of Artificial Intelligence and Soft Computing Research, 2019, 9 (2), pp.149-165. ⟨10.2478/jaiscr-2018-0030⟩. ⟨hal-02994813⟩
68 Consultations
0 Téléchargements

Altmetric

Partager

More