1. bookTom 9 (2019): Zeszyt 2 (April 2019)
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2449-6499
Pierwsze wydanie
30 Dec 2014
Częstotliwość wydawania
4 razy w roku
Języki
Angielski
access type Otwarty dostęp

Impact of Learners’ Quality and Diversity in Collaborative Clustering

Data publikacji: 31 Dec 2018
Tom & Zeszyt: Tom 9 (2019) - Zeszyt 2 (April 2019)
Zakres stron: 149 - 165
Otrzymano: 28 Jan 2018
Przyjęty: 03 Jul 2018
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2449-6499
Pierwsze wydanie
30 Dec 2014
Częstotliwość wydawania
4 razy w roku
Języki
Angielski
Abstract

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.

Keywords

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