dc.contributor.author | Papadopoulos, Antonios I. | en |
dc.contributor.author | Rafailidis, D. | en |
dc.contributor.author | Pallis, George C. | en |
dc.contributor.author | Dikaiakos, Marios D. | en |
dc.contributor.editor | Toumani F. | en |
dc.contributor.editor | Decker H. | en |
dc.contributor.editor | Chen Q. | en |
dc.contributor.editor | Wagner R. | en |
dc.contributor.editor | Hameurlain A. | en |
dc.creator | Papadopoulos, Antonios I. | en |
dc.creator | Rafailidis, D. | en |
dc.creator | Pallis, George C. | en |
dc.creator | Dikaiakos, Marios D. | en |
dc.date.accessioned | 2019-11-13T10:41:42Z | |
dc.date.available | 2019-11-13T10:41:42Z | |
dc.date.issued | 2015 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54718 | |
dc.description.abstract | Attributed multi-graphs are data structures to model realworld networks of objects which have rich properties/attributes and they are connected by multiple types of edges. Clustering attributed multigraphs has several real-world applications, such as recommendation systems and targeted advertisement. In this paper, we propose an efficient method for Clustering Attributed Multi-graphs with Information Ranking, namely CAMIR. We introduce an iterative algorithm that ranks the different vertex attributes and edge-types according to how well they can separate vertices into clusters. The key idea is to consider the ‘agreement’ among the attribute- and edge-types, assuming that two vertex properties ‘agree’ if they produced the same clustering result when used individually. Furthermore, according to the calculated ranks we construct a unified similarity measure, by down-weighting noisy vertex attributes or edge-types that may reduce the clustering accuracy. Finally, to generate the final clusters, we follow a spectral clustering approach, suitable for graph partitioning and detecting arbitrary shaped clusters. In our experiments with synthetic and real-world datasets, we show the superiority of CAMIR over several state-of-the-art clustering methods. © Springer International Publishing Switzerland 2015. | en |
dc.source | 26th International Conference on Database and Expert Systems Applications, DEXA 2015 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984643516&doi=10.1007%2f978-3-319-22849-5_29&partnerID=40&md5=3c0dc55bd2a3d67b6a7139fa03ccf2ef | |
dc.subject | Algorithms | en |
dc.subject | Iterative methods | en |
dc.subject | Iterative algorithm | en |
dc.subject | Graphic methods | en |
dc.subject | Clustering algorithms | en |
dc.subject | Expert systems | en |
dc.subject | Real-world datasets | en |
dc.subject | Attributed multi-graphs | en |
dc.subject | Clustering accuracy | en |
dc.subject | Information ranking | en |
dc.subject | Real-world networks | en |
dc.subject | Spectral clustering | en |
dc.subject | Targeted advertisements | en |
dc.title | Clustering attributed multi-graphs with information ranking | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1007/978-3-319-22849-5_29 | |
dc.description.volume | 9261 | |
dc.description.startingpage | 432 | |
dc.description.endingpage | 446 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Sponsors: | en |
dc.description.notes | Conference code: 139739 | en |
dc.description.notes | Cited By :1</p> | en |
dc.source.abbreviation | Lect. Notes Comput. Sci. | en |
dc.contributor.orcid | Pallis, George C. [0000-0003-1815-5468] | |
dc.contributor.orcid | Dikaiakos, Marios D. [0000-0002-4350-6058] | |
dc.gnosis.orcid | 0000-0003-1815-5468 | |
dc.gnosis.orcid | 0000-0002-4350-6058 | |