| 数据挖掘中关联规则算法的研究及应用 |
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| 引用本文： | 高杰,李绍军,钱锋.数据挖掘中关联规则算法的研究及应用[J].东南大学学报(自然科学版),2006(Z1). |
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| 作者姓名： | 高杰 李绍军 钱锋 |
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| 作者单位： | [1]华东理工大学自动化研究所 [2]上海 |
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| 摘 要： | 针对Apriori和AprioriTid算法中存在的项集生成瓶颈问题,提出了一种基于事务集压缩、候选项集压缩和支持度布尔矩阵的改进AprioriTid算法.该算法中通过删去不必比较的事务来有效缩减数据集;优化频繁项集的自连接方式来减少生成的候选项集个数;使用支持度布尔矩阵来加快候选项集的验证速度.实验结果表明改进算法比原算法执行效率明显提高,同时也验证了该算法在油液监测故障诊断中的有效性.
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| 关 键 词： | 数据挖掘 关联规则 Apriori算法 AprioriTid算法 频繁项集 故障诊断 |
Application of association rules algorithm in data mining |
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| Gao Jie Li Shaojun Qian Feng.Application of association rules algorithm in data mining[J].Journal of Southeast University(Natural Science Edition),2006(Z1). |
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| Authors: | Gao Jie Li Shaojun Qian Feng |
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| Abstract: | An improved AprioriTid(IAT) algorithm is proposed,which is based on transactions reduction,candidate itemsets reduction and support matrix to solve the bottleneck of itemsets generation.This method minimizes the database by deleting transactions which need not be scanned,reduces the number of candidate itemsets by optimizing the joint procedure of frequent itemsets and accelerates the verification speed of candidate itemsets by using the support matrix method.Experimental results prove the high efficiency of the proposed algorithm.The simulation results of oil monitoring for fault diagnosis also show the validity of the IAT algorithm. |
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| Keywords: | data mining association rules Apriori algorithm AprioriTid algorithm frequent itemsets fault diagnosis |
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