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dc.contributor.authorCuzzocrea, Alfredo
dc.contributor.authorHan, Zhao
dc.contributor.authorJiang, Fan
dc.contributor.authorLeung, Carson K.
dc.contributor.authorZhang, Hao
dc.date.accessioned2016-03-08T21:49:13Z
dc.date.available2016-03-08T21:49:13Z
dc.date.issued2015
dc.identifier.citationA. Cuzzocrea, Z. Han, F. Jiang, C.K. Leung, H. Zhang. Edge-based mining of frequent subgraphs from graph streams. Procedia Computer Science, 60 (2015), pp. 573-582.en_US
dc.identifier.urihttp://hdl.handle.net/1993/31149
dc.descriptionA. Cuzzocrea, Z. Han, F. Jiang, C.K. Leung, H. Zhang. Edge-based mining of frequent subgraphs from graph streams. Procedia Computer Science, 60 (2015), pp. 573-582. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.description.abstractIn the current era of Big data, high volumes of valuable data can be generated at a high velocity from high-varieties of data sources in various real-life applications ranging from sensor networks to social networks, from bio-informatics to chemical informatics. In addition, Big data are also available in business, education, engineering, finance, healthcare, scientific, telecommunication, and transportation domains. A collection of these data can be viewed as a big dynamic graph structure. Embedded in them are implicit, previously unknown, and potentially useful knowledge. Consequently, efficient knowledge discovery algorithms for mining frequent subgraphs from these dynamic streaming graph structured data are in demand. On the one hand, some existing algorithms discover collections of frequently co-occurring edges, which may be disjoint. On the other hand, some other existing algorithms discover frequent subgraphs by requiring very large memory space. With high volumes of Big data, available memory space may be limited. To discover collections of frequently co-occurring connected edges, we present in this paper two efficient algorithms that require small memory space. Evaluation results show the efficiency of our edge-based algorithms in mining frequent subgraphs from graph streams.en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (NSERC); University of Manitobaen_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectknowledge discovery and data miningen_US
dc.subjectfrequent patternsen_US
dc.subjectfrequent subgraphsen_US
dc.subjectgraph structured dataen_US
dc.subjectdata streamsen_US
dc.titleEdge-based mining of frequent subgraphs from graph streamsen_US
dc.typeArticleen_US
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doihttp://dx.doi.org/10.1016/j.procs.2015.08.184


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