All-Weighted Positive and Negative Association Rules Mining Based on Dynamic Item Weight
ZHOU Xiu-mei1, HUANG Ming-xuan2
1. Department of Mathematics and Computer Science, Nanning Prefecture Education College, Chongzuo, Guangxi 532200, China;
2. School of Information and Statistics, Guangxi University of Finance and Economics, Nanning, Guangxi 530003, China
This paper proposes a mining algorithm of all-weighted positive and negative association rules based on dynamic item weight, which can solve the problems of negative patterns mining based on dynamic item weight.This algorithm took the dynamic item weight dependent on transaction records into consideration, and adopted the itemset pruning method and pattern evaluation framework so as to discover effective all-weighted positive & negative association rules via simple calculation and comparison of weight ratio and dimension ratio from the itemset.The experimental results show that this algorithm can prevent ineffective patterns, which makes the maximal declines of the mining time and number of the candidate itemsets by up to 94.09% and 88.16% respectively compared with the existing unweighted positive and negative association rule mining algorithms.
周秀梅, 黄名选. 基于项权值变化的完全加权正负关联规则挖掘[J]. 电子学报, 2015, 43(8): 1545-1554.
ZHOU Xiu-mei, HUANG Ming-xuan. All-Weighted Positive and Negative Association Rules Mining Based on Dynamic Item Weight. Chinese Journal of Electronics, 2015, 43(8): 1545-1554.
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