| 动态关联规则的趋势度挖掘方法 |
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| 引用本文： | 张忠林 曾庆飞 许凡. 动态关联规则的趋势度挖掘方法[J]. 计算机应用, 2012, 32(1): 196-198. DOI: 10.3724/SP.J.1087.2012.00196 |
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| 作者姓名： | 张忠林 曾庆飞 许凡 |
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| 作者单位： | 兰州交通大学 电子与信息工程学院，兰州 730070 |
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| 基金项目： | 国家自然科学基金资助项目(61163010);甘肃省科技支撑计划项目(1011GKCA040) |
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| 摘 要： | 针对规则随着时间变化的特点，在分析原有定义和对支持度向量(SV)和置信度向量分类的基础上，提出了动态关联规则趋势度的挖掘方法。首先，利用趋势度阈值消除无价值的规则，减小候选项集；其次，产生动态关联规则的趋势度元规则，找出具有价值的规则，提高挖掘质量；最后，通过对具有增减和周期趋势的事物数据库分析，证明了所提方法的有效性。
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| 关 键 词： | 数据挖掘 动态关联规则 趋势度 元关联规则 |
| 收稿时间： | 2011-07-15 |
| 修稿时间： | 2011-09-23 |
Method of data tendency measure mining in dynamic association rules |
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| ZHANG Zhong-lin ZENG Qing-fei XU Fan. Method of data tendency measure mining in dynamic association rules[J]. Journal of Computer Applications, 2012, 32(1): 196-198. DOI: 10.3724/SP.J.1087.2012.00196 |
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| Authors: | ZHANG Zhong-lin ZENG Qing-fei XU Fan |
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| Affiliation: | School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou Gansu 730070, China |
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| Abstract: | Based on the original definition and classification of Support Vector (SV) and confidence vector, this paper put forward a method of data tendency measure mining in dynamic association rules, according to the characteristic of rules with time changing. First, taking advantage of tendency measure threshold to eliminate useless rules, the item sets candidates can be reduced. Second, producing the dynamic association rule, this method found out valuable rules and improved the mining quality. Finally, by analyzing a transaction database that is characterized by the tendency of changes and cycles, the analytical results verify the validity of the proposed method. |
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| Keywords: | data mining dynamic association rule tendency measure meta-association rule |
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