DocumentCode :
3351179
Title :
WTMaxMiner: Efficient mining of Maximal Frequent Patterns based on Weighted Directed Graph Traversals
Author :
Geng, Runian ; Dong, Xiangjun ; Zhang, Ping ; Xu, Wenbo
Author_Institution :
Sch. of Inf. Technol., Jiangnan Univ., Wuxi
fYear :
2008
fDate :
21-24 Sept. 2008
Firstpage :
1081
Lastpage :
1086
Abstract :
Frequent itemset mining for traversal patterns have been found useful in several applications. However, (closed) frequent mining can generate huge and redundant patterns, and traditional model of traversal patterns mining considered only un-weighted traversals. In this paper, a transformable model between EWDG (Edge-Weighted Directed Graph) and VWDG (Vertex-Weighted Directed Graph) is proposed. Based on the model, an effective algorithm, called WTMaxMiner (Weighted Traversals-based Maximal Frequent Patterns Miner), is developed to discover maximal weighted frequent patterns from weighted traversals on directed graph. Experimental comparison results with previous work on synthetic data show that the algorithm has a good performance and scalable property to the problem of mining maximal frequent patterns based on weighted graph traversals.
Keywords :
data mining; directed graphs; WTMaxMiner; edge-weighted directed graph; maximal frequent patterns; vertex-weighted directed graph; weighted directed graph traversals; Data mining; Databases; Information science; Information technology; Itemsets; Joining processes; Navigation; Performance analysis; Web pages; World Wide Web; closed pattern mining; data mining; maximal weighted frequent pattern mining; traversal patterns;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cybernetics and Intelligent Systems, 2008 IEEE Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-1673-8
Electronic_ISBN :
978-1-4244-1674-5
Type :
conf
DOI :
10.1109/ICCIS.2008.4670858
Filename :
4670858
Link To Document :
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