By Topic

Efficiently Mining Maximal Frequent Patterns from Traversals on Weighted Directed Graph Using Statistical Theory

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Runian Geng ; Sch. of Inf. Technol., Jiangnan Univ., Wuxi ; Xiangjun Dong ; Guoling Liu ; Wenbo Xu

To solve the problem of mining weighted patterns with noisy weight from traversals on weighted directed graph (WDG), an effective algorithm, called SMaxWFPMiner (Statistical theory-based maximal weighted frequent patterns miner), is proposed. The algorithm undergoes two phases to discover MaxWFP from the traversals on WDG. In the first phase, it adopts the weightpsilas confidence level (CL) to remove the vertices with noisy weights, which reduce remarkably the size of traversal database (TDB). In the second phase, incorporating the maximal property with weight constrains, it exploits a weighted FP-tree approach to reduce effectively search space and extract succinct and lossless patterns from weighted graph TDB. Experimental comparison results show that the algorithm is efficient and scalable for mining MaxWFPs based on traversals on the WDG.

Published in:

Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on  (Volume:2 )

Date of Conference:

18-20 Oct. 2008