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Received:May 29, 2008 Revised:October 09, 2008
Received:May 29, 2008 Revised:October 09, 2008
Abstract:This paper studies uncertain graph data mining and especially investigates the problem of mining frequent subgraph patterns from uncertain graph data. A data model is introduced for representing uncertainties in graphs, and an expected support is employed to evaluate the significance of subgraph patterns. By using the apriori property of expected support, a depth-first search-based mining algorithm is proposed with an efficient method for computing expected supports and a technique for pruning search space, which reduces the number of subgraph isomorphism testings needed by computing expected support from the exponential scale to the linear scale. Experimental results show that the proposed algorithm is 3 to 5 orders of magnitude faster than a na?ve depth-first search algorithm, and is efficient and scalable.
Foundation items:
Author Name | Affiliation |
ZOU Zhao-Nian | 哈尔滨工业大学 计算机科学与技术学院,黑龙江 哈尔滨 150001 |
LI Jian-Zhong | |
GAO Hong | |
ZHANG Shuo |
Reference text:
ZOU Zhao-Nian,LI Jian-Zhong,GAO Hong,ZHANG Shuo.Mining Frequent Subgraph Patterns from Uncertain Graphs.Journal of Software,2009,20(11):2965-2976
ZOU Zhao-Nian,LI Jian-Zhong,GAO Hong,ZHANG Shuo.Mining Frequent Subgraph Patterns from Uncertain Graphs.Journal of Software,2009,20(11):2965-2976