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<identifier>oai:HAL:hal-01312313v1</identifier>
<datestamp>2017-12-21</datestamp>
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<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>Matching of communities and frequent conceptual links</title>
<creator>Stattner, Erick</creator>
<creator>Collard, Martine</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>IDC ; Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG) - Université des Antilles et de la Guyane (UAG)</contributor>
<description>International audience</description>
<source>ISSN: 1869-5450</source>
<source>EISSN: 1869-5469</source>
<source>Social Network Analysis and Mining</source>
<publisher>Springer</publisher>
<identifier>hal-01312313</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01312313</identifier>
<source>https://hal.archives-ouvertes.fr/hal-01312313</source>
<source>Social Network Analysis and Mining, Springer, 2014, 〈10.1007/s13278-014-0197-1〉</source>
<identifier>DOI : 10.1007/s13278-014-0197-1</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1007/s13278-014-0197-1</relation>
<language>en</language>
<subject lang=en> Frequent conceptual links</subject>
<subject lang=en> Clustering Frequent patterns</subject>
<subject lang=en>Social network Social network mining</subject>
<subject lang=en> Communities</subject>
<subject>[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]</subject>
<subject>[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>he search for frequent conceptual links (FCL) is a new network clustering approach that exploits both the network structure and the node properties. While recent works have been focused on the optimisation of the FCL extraction, no study has been conducted on the matching or the intersections between the conceptual links and the classical clustering approach which consists in extracting communities. In this paper, we focus on these two approaches. Our objective is to evaluate the possible relationships existing between FCL and communities to understand how the patterns obtained by each kind of methods may match as well as the useful knowledge resulting from the intersections of these two kinds of knowledge. For this purpose, we propose a set of original measures, based on the notion of homogeneity, assessing the level of shared information between these patterns when they are extracted from the same dataset. The measures are applied on three datasets and demonstrate the importance of considering simultaneously various kinds of knowledge and their intersections.</description>
<date>2014-05</date>
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