- Tytuł:
- Impact of learners’ quality and diversity in collaborative clustering
- Autorzy:
-
Rastin, Parisa
Matei, Basarab
Cabanes, Guénaël
Grozavu, Nistor
Bennani, Younés - Powiązania:
- https://bibliotekanauki.pl/articles/91600.pdf
- Data publikacji:
- 2019
- Wydawca:
- Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
- Tematy:
-
collaborative clustering
topological neural networks
unsupervised learning
diversity
quality - Opis:
- 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.
- Źródło:
-
Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 2; 149-165
2083-2567
2449-6499 - Pojawia się w:
- Journal of Artificial Intelligence and Soft Computing Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł