<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Impact of Learners’ Quality and Diversity in Collaborative Clustering

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Impact of Learners\u2019 Quality and Diversity in Collaborative Clustering"}

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...

Impact of Learners’ Quality and Diversity in Collaborative Clustering

Data publikacji: 31 gru 2018

Zakres stron: 149 - 165

Otrzymano: 28 sty 2018

Przyjęty: 03 lip 2018

DOI: https://doi.org/10.2478/jaiscr-2018-0030

Słowa kluczowe
collaborative clustering, topological neural networks, unsupervised learning, diversity, quality

© 2018 Parisa Rastin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Impact of Learners’ Quality and Diversity in Collaborative Clustering

Data publikacji: 31 gru 2018

Zakres stron: 149 - 165

Otrzymano: 28 sty 2018

Przyjęty: 03 lip 2018

DOI: https://doi.org/10.2478/jaiscr-2018-0030

Słowa kluczowecollaborative clustering, topological neural networks, unsupervised learning, diversity, quality

© 2018 Parisa Rastin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
collaborative clustering, topological neural networks, unsupervised learning, diversity, quality