Frequent pattern mining from dense graph streams

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dc.contributor.authorCameron, Juan J.
dc.contributor.authorCuzzocrea, Alfredo
dc.contributor.authorJiang, Fan
dc.contributor.authorLeung, Carson K.
dc.date.accessioned2017-02-13T16:01:08Z
dc.date.available2017-02-13T16:01:08Z
dc.date.issued2014
dc.descriptionJ.J. Cameron, A. Cuzzocrea, F. Jiang, & C.K. Leung. Frequent pattern mining from dense graph streams. In Proc. EDBT/ICDT Workshops 2014, pp. 240-247. This paper is published in the Workshop Proceedings of the EDBT/ICDT 2014 Joint Conference (March 28, 2014, Athens, Greece) on CEUR-WS.org (ISSN 1613-0073) under the terms of the Creative Commons license CC-by-nc-nd 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0).en_US
dc.description.abstractAs technology advances, streams of data can be produced in many applications such as social networks, sensor networks, bioinformatics, and chemical informatics. These kinds of streaming data share a property in common--namely, they can be modeled in terms of graph-structured data. Here, the data streams generated by graph data sources in these applications are graph streams. To extract implicit, previously unknown, and potentially useful frequent patterns from these streams, efficient data mining algorithms are in demand. Many existing algorithms capture important streaming data and assume that the captured data can fit into main memory. However, problems arise when such an assumption does not hold (e.g., when the available memory is limited). In this paper, we propose a data structure called DSMatrix for capturing important data from the streams--especially, dense graph streams--onto the disk when the memory space is limited. In addition, we also propose two stream mining algorithms that use DSMatrix to mine frequent patterns. The tree-based horizontal mining algorithm applies an effective frequency counting approach to avoid recursive construction of sub-trees as in many tree-based mining. The vertical mining algorithm makes good use of the information captured in the DSMatrix for mining.en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (NSERC); University of Manitobaen_US
dc.identifier.otherhttp://ceur-ws.org/Vol-1133/paper-39.pdf
dc.identifier.urihttp://hdl.handle.net/1993/32126
dc.language.isoengen_US
dc.publisherCEUR Workshop Proceedingsen_US
dc.relation.ispartofseriesCEUR Workshop Proceedings (ISSN 1613-0073);Vol. 1133
dc.rightsopen accessen_US
dc.subjectdata miningen_US
dc.subjectfrequent pattern discoveryen_US
dc.subjectgraph patternsen_US
dc.subjectgraph-structured dataen_US
dc.subjectsocial networksen_US
dc.subjectextending database technologyen_US
dc.subjectdatabase theoryen_US
dc.titleFrequent pattern mining from dense graph streamsen_US
dc.typeArticleen_US
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