Impact of learners’ quality and diversity in collaborative clustering

• Autorzy: Rastin Parisa , Matei Basarab , Cabanes Guénaël , Grozavu Nistor , Bennani Younés

Identyfikatory

DOI

10.2478/jaiscr-2018-0030

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

collaborative clustering; topological neural networks; unsupervised learning; diversity; quality

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2019
• Tom: Vol. 9, No. 2
• Strony: 149--165
• Opis fizyczny: Bibliogr. 41 poz., rys.

Twórcy

autor

Rastin Parisa

• LIPN, UMR CNRS 7030, Institut Galile Universit Paris 13 99, avenue Jean-Baptiste Clment, 93430 Villetaneuse

autor

Matei Basarab

• LIPN, UMR CNRS 7030, Institut Galile Universit Paris 13 99, avenue Jean-Baptiste Clment, 93430 Villetaneuse

autor

Cabanes Guénaël

• LIPN, UMR CNRS 7030, Institut Galile Universit Paris 13 99, avenue Jean-Baptiste Clment, 93430 Villetaneuse

autor

Grozavu Nistor

• LIPN, UMR CNRS 7030, Institut Galile Universit Paris 13 99, avenue Jean-Baptiste Clment, 93430 Villetaneuse

autor

Bennani Younés

• LIPN, UMR CNRS 7030, Institut Galile Universit Paris 13 99, avenue Jean-Baptiste Clment, 93430 Villetaneuse

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).