NDLI: Frequent Subgraph Mining from Streams of Uncertain Data

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Frequent Subgraph Mining from Streams of Uncertain Data

Content Provider	ACM Digital Library
Author	Cuzzocrea, Alfredo Leung, Carson K.
Abstract	In the current era of Big data, high volumes of high-value data---such as social network data---can be generated at a high velocity. The quality and accuracy of these data depend on their veracity: uncertainty of the data. A collection of these uncertain data can be viewed as a big, interlinked, dynamic graph structure. Embedded in these big data are implicit, previously unknown, and potentially useful knowledge. Hence, efficient and effective knowledge discovery algorithms for mining frequent subgraphs from these dynamic streaming graph structured data are in demand. Most of the existing algorithms mine frequent subgraph from streams of precise data. However, there are many real-life scientific and engineering applications, in which data are uncertain. Hence, in this paper, we propose algorithms that use limited memory space for mining frequent subgraphs from streams of uncertain data. Evaluation results show the effectiveness of our algorithms in mining frequent subgraphs from streams of uncertain data.
Starting Page	18
Ending Page	27
Page Count	10
File Format	PDF
ISBN	9781450334198
DOI	10.1145/2790798.2790799
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-07-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Frequent patterns Frequent subgraphs Data streams Data mining Uncertain data
Content Type	Text
Resource Type	Article

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