计算机应用 ›› 2014, Vol. 34 ›› Issue (10): 2820-2826.DOI: 10.11772/j.issn.1001-9081.2014.10.2820
收稿日期:
2014-04-15
修回日期:
2014-06-12
出版日期:
2014-10-01
发布日期:
2014-10-30
通讯作者:
黄名选
作者简介:
基金资助:
国家自然科学基金资助项目;广西自然科学基金资助项目;广西教育厅科研项目;广西高校优秀人才资助计划项目
ZHOU Xiumei1,HUANG Mingxuan2
Received:
2014-04-15
Revised:
2014-06-12
Online:
2014-10-01
Published:
2014-10-30
Contact:
HUANG Mingxuan
摘要:
针对现有加权关联规则挖掘算法不能适用于矩阵加权数据的缺陷,给出一种新的矩阵加权项集剪枝策略,构建矩阵加权正负关联模式评价框架SRCCCI,提出一种新的基于SRCCCI评价框架的矩阵加权正负关联规则挖掘算法MWARM-SRCCCI。该算法克服了现有挖掘技术的缺陷,采用新的剪枝技术和模式评价方法,挖掘有效的矩阵加权正负关联规则,避免一些无效和无趣的模式产生。以中文Web测试集CWT200g为实验数据,与现有无加权正负关联规则挖掘算法比较,MWARM-SRCCCI算法的挖掘时间减幅最大可达74.74%。理论分析和实验结果表明,MWARM-SRCCCI算法具有较好的剪枝效果,候选项集数量和挖掘时间明显减少,挖掘效率得到极大提高,其关联模式可为信息检索提供可靠的查询扩展词来源。
中图分类号:
周秀梅 黄名选. 有效的矩阵加权正负关联规则挖掘算法——MWARM-SRCCCI[J]. 计算机应用, 2014, 34(10): 2820-2826.
ZHOU Xiumei HUANG Mingxuan. MWARM-SRCCCI :efficient algorithm for mining matrix-weighted positive and negative association rules[J]. Journal of Computer Applications, 2014, 34(10): 2820-2826.
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