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dc.contributor.author Cameron, Juan J.
dc.contributor.author Cuzzocrea, Alfredo
dc.contributor.author Jiang, Fan
dc.contributor.author Leung, Carson K.
dc.date.accessioned 2017-02-13T16:01:08Z
dc.date.available 2017-02-13T16:01:08Z
dc.date.issued 2014
dc.identifier.other http://ceur-ws.org/Vol-1133/paper-39.pdf
dc.identifier.uri http://hdl.handle.net/1993/32126
dc.description J.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.abstract As 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.sponsorship Natural Sciences and Engineering Research Council of Canada (NSERC); University of Manitoba en_US
dc.language.iso en en_US
dc.publisher CEUR Workshop Proceedings en_US
dc.relation.ispartofseries CEUR Workshop Proceedings (ISSN 1613-0073);Vol. 1133
dc.subject data mining en_US
dc.subject frequent pattern discovery en_US
dc.subject graph patterns en_US
dc.subject graph-structured data en_US
dc.subject social networks en_US
dc.subject extending database technology en_US
dc.subject database theory en_US
dc.title Frequent pattern mining from dense graph streams en_US
dc.type Article en_US


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