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2018 | Supplement | Buchkapitel

Community Detection in Bibsonomy Using Data Clustering

verfasst von : Zakaria Saoud, Jan Platoš

Erschienen in: Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017

Verlag: Springer International Publishing

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Abstract

Community detection aims to extract the related groups of nodes from complex networks, by exploiting the network topology. Different approaches have been proposed for community detection, where most of them are based on clustering algorithms. In this paper we investigate how we can use the clustering for the community detection in the academic social bookmarking website: Bibsonomy. Our goal is to determine the most suitable clustering algorithm for similar user detection in Bibsonomy. To realize that, we have compared three clustering algorithms: The k-means, the k-medoids and the Agglomerative clustering algorithms. Experimental results demonstrate that k-means performs better than the other algorithms, for community detection in Bibsonomy.

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Vorheriges Kapitel PID-Fuzzy Controllers with Dynamic Structure and Evolutionary Method for Their Construction
Nächstes Kapitel Using Predictive Data Mining Models for Data Analysis in a Logistics Company
Fußnoten
1
Knowledge and Data Engineering Group, University of Kassel: Benchmark Folksonomy Data from BibSonomy, version of January 01st, 2016. http://​bibsonomy.​org/​.
 
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Metadaten
Titel
Community Detection in Bibsonomy Using Data Clustering
verfasst von
Zakaria Saoud
Jan Platoš
Copyright-Jahr
2018
Buch
Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017

Print ISBN: 978-3-319-67219-9

Electronic ISBN: 978-3-319-67220-5

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-67220-5_14

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