Effectively and efficiently mining frequent patterns from dense graph streams on disk
Loading...
Date
2014
Authors
Braun, Peter
Cameron, Juan J.
Cuzzocrea, Alfredo
Jiang, Fan
Leung, Carson K.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
In this paper, we focus on dense graph streams, which can be generated in various applications ranging from sensor networks to social networks, from bio-informatics to chemical informatics. We also investigate the problem of effectively and efficiently mining frequent patterns from such streaming data, in the targeted case of dealing with limited memory environments so that disk support is required. This setting occurs frequently (e.g., in mobile applications/systems) and is gaining momentum even in advanced computational settings where social networks are the main representative. Inspired by this problem, we propose (i) a specialized data structure called DSMatrix, which captures important data from dense graph streams onto the disk directly and (ii) stream mining algorithms that make use of such structure in order to mine frequent patterns effectively and efficiently. Experimental results clearly confirm the benefits of our approach.
Description
P. Braun, J.J. Cameron, A. Cuzzocrea, F. Jiang, C.K. Leung. Effectively and efficiently mining frequent patterns from dense graph streams on disk. Procedia Computer Science, 35 (2014), pp. 338-347. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords
data mining, frequent pattern mining, graph streams, knowledge-based and intelligent information & engineering systems, knowledge discovery, limited memory, stream mining
Citation
P. Braun, J.J. Cameron, A. Cuzzocrea, F. Jiang, C.K. Leung. Effectively and efficiently mining frequent patterns from dense graph streams on disk. Procedia Computer Science, 35 (2014), pp. 338-347.