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Ιδρυματικό Αποθετήριο [SANDBOX]
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| URI | http://purl.tuc.gr/dl/dias/38174108-5054-4A25-89F1-56A8E90CA97D | - |
| Αναγνωριστικό | https://doi.org/10.1145/1458469.1458480 | - |
| Γλώσσα | en | - |
| Τίτλος | A measure for cluster cohesion in semantic overlay networks | en |
| Δημιουργός | Petrakis Evripidis | en |
| Δημιουργός | Πετρακης Ευριπιδης | el |
| Δημιουργός | Paraskevi Raftopoulou | en |
| Εκδότης | Association for Computing Machinery | en |
| Περίληψη | Semantic overlay networks cluster peers that are semantically, thematically or socially close into groups by means of a rewiring procedure that is periodically executed by each peer. Rewiring proceeds by establishing new connections to similar peers, and by discarding connections that are outdated or pointing to dissimilar peers. This process aims at improving cluster quality (how well peers with similar content are clustered together) and by this, at improving the flow of information in the network by reducing the number of messages that are exchanged. Therefore, measuring the quality of clustering is an important issue by itself. This is exactly the issue this work is dealing with. In this paper, we introduce a new clustering measure that takes into account the whole neighborhood of a peer (rather than its direct neighbors) thus, providing better insight on the quality of the underlying clustered organisation. Our experimental evaluation with real-word data and queries confirms our assumption that the new measure is better suited for measuring clustering quality than other known measures, such as the (generalised) clustering coefficient. | en |
| Τύπος | Περίληψη Δημοσίευσης σε Συνέδριο | el |
| Τύπος | Conference Paper Abstract | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-11-01 | - |
| Ημερομηνία Δημοσίευσης | 2008 | - |
| Βιβλιογραφική Αναφορά | Paraskevi Raftopoulou, Euripides G.M. Petrakis, "A Measure for Cluster Cohesion in Semantic Overlay Networks", in 6th Workshop on Large-Scale Distributed Systems for Information Retrieval (LSDS-IR'08), 2008, pp.59-66. doi:10.1145/1458469.1458480 | en |
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