| 基于支持度矩阵的Apriori改进算法 |
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| 引用本文： | 洪梓璇,边馥苓.基于支持度矩阵的Apriori改进算法[J].武汉大学学报(信息科学版),2008,33(12):1246-1249. |
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| 作者姓名： | 洪梓璇 边馥苓 |
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| 作者单位： | 武汉大学测绘遥感信息工程国家重点实验室，武汉大学空间信息与数字工程研究中心 |
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| 基金项目： | 地理信息系统教育部重点实验室开放研究基金 |
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| 摘 要： | 提出了一种利用支持度矩阵生成频繁项集的Apriori改进算法。通过上三角分块稀疏矩阵的行列性质和非频繁列之间的约束关系,改进的算法避免了Apriori连接步中大量非频繁候选k-项集的产生及其在剪枝步中(k-1)-子集的分解和判断。该算法能够有效地压缩搜索空间,降低Apriori连接和剪枝步骤的开销。
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| 关 键 词： | Apriori 关联规则 数据挖掘 |
An Improved Apriori Algorithm Based on Support Count Matrix |
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| HONG Zixuan,BIAN Fuling.An Improved Apriori Algorithm Based on Support Count Matrix[J].Geomatics and Information Science of Wuhan University,2008,33(12):1246-1249. |
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| Authors: | HONG Zixuan BIAN Fuling |
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| Abstract: | An improved Apriori algorithm which uses support count matrix to generate frequent itemsets is proposed.It avoids the generation of a large amount of non-frequent candidate k-itemsets in the join step and the judgments of(k-1)-subset in the prune step by the characteristics of rows and columns in the upper triangular matrix which is partitioned and sparse,and by the restrict relations between non-frequent columns.It effectively compresses the search space and reduces the computational cost of the Apriori algorithm. |
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| Keywords: | Apriori association rule data mining |
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