Community Detection in Bibsonomy Using Data Clustering
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10246267" target="_blank" >RIV/61989100:27240/18:10246267 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-67220-5_14" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-67220-5_14</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-67220-5_14" target="_blank" >10.1007/978-3-319-67220-5_14</a>
Alternative languages
Result language
angličtina
Original language name
Community Detection in Bibsonomy Using Data Clustering
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
INFORMATION SYSTEMS ARCHITECTURE AND TECHNOLOGY, PT I
ISBN
978-3-319-67220-5
ISSN
2194-5357
e-ISSN
2194-5365
Number of pages
10
Pages from-to
149-158
Publisher name
SPRINGER INTERNATIONAL PUBLISHING AG
Place of publication
CHAM
Event location
Szklarska Poreba
Event date
Sep 17, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000452449500014