Community Detection in Bibsonomy Using Data Clustering

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Community Detection in Bibsonomy Using Data Clustering

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Community Detection in Bibsonomy Using Data Clustering

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

INFORMATION SYSTEMS ARCHITECTURE AND TECHNOLOGY, PT I

ISBN

978-3-319-67220-5

ISSN

2194-5357

e-ISSN

2194-5365

Počet stran výsledku

10

Strana od-do

149-158

Název nakladatele

SPRINGER INTERNATIONAL PUBLISHING AG

Místo vydání

CHAM

Místo konání akce

Szklarska Poreba

Datum konání akce

17. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000452449500014