All-Weighted Positive and Negative Association Rules Mining Based on Dynamic Item Weight

ZHOU Xiu-mei; HUANG Ming-xuan

doi:10.3969/j.issn.0372-2112.2015.08.012

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All-Weighted Positive and Negative Association Rules Mining Based on Dynamic Item Weight

更新时间：2025-07-16

- All-Weighted Positive and Negative Association Rules Mining Based on Dynamic Item Weight
- Acta Electronica Sinica Vol. 43, Issue 8, Pages: 1545-1554(2015)
- 作者机构：
  
  1. 南宁地区教育学院数学与计算机科学系,广西,崇左,532200
  2. 广西财经学院信息与统计学院,广西,南宁,530003
  3. 南宁地区教育学院数学与计算机科学系,广西,崇左,532200
  4. 广西财经学院信息与统计学院,广西,南宁,530003
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2015.08.012
  CLC： TP391
- Published：2015
- 稿件说明：
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ZHOU Xiu-mei, HUANG Ming-xuan. All-Weighted Positive and Negative Association Rules Mining Based on Dynamic Item Weight[J]. Acta Electronica Sinica, 2015, 43(8): 1545-1554.
DOI：

ZHOU Xiu-mei, HUANG Ming-xuan. All-Weighted Positive and Negative Association Rules Mining Based on Dynamic Item Weight[J]. Acta Electronica Sinica, 2015, 43(8): 1545-1554. DOI： 10.3969/j.issn.0372-2112.2015.08.012.

摘要

本文提出一种基于项权值变化的完全加权正负关联规则挖掘算法

解决了基于项权值变化的负模式挖掘问题.该算法考虑项权值依赖于事务记录的特点

采用新的项集剪枝方法和模式评价框架

通过项集的项内权值比和维数比的简单计算和比较

挖掘有效的完全加权正负关联规则.实验结果表明

与现有无加权正负关联规则挖掘算法比较

本文算法能避免无效的模式出现

其挖掘时间和候选项集数量明显减少

减幅最大分别可达94.09%和88.16%.

Abstract

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.

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