A Graph-Based Approach for Discovering Various Types of Association Rules
A Graph-Based Approach for Discovering Various Types of Association Rules
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DOI：
10.1109/69.956106
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发表时间：
2001-09
期刊：
影响因子：
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通讯作者：
Show-Jane Yen;Arbee L. P. Chen
中科院分区：
文献类型：
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作者：
Show-Jane Yen;Arbee L. P. Chen
挖掘关联规则是知识发现的一项重要任务。我们可以分析过去的交易数据来发现客户行为，从而提高业务决策的质量。大型客户交易数据库中可能存在各种类型的关联规则。挖掘关联规则的策略侧重于发现大项目集，这些项目集是在足够数量的交易中一起出现的项目组。我们提出了一种基于图的方法，从大型客户交易数据库中生成各种类型的关联规则。这种方法扫描数据库一次以构建关联图，然后遍历该图以生成所有大型项目集。实证评估表明，我们的算法优于需要多次遍历数据库的其他算法。
Mining association rules is an important task for knowledge discovery. We can analyze past transaction data to discover customer behaviors such that the quality of business decisions can be improved. Various types of association rules may exist in a large database of customer transactions. The strategy of mining association rules focuses on discovering large item sets, which are groups of items which appear together in a sufficient number of transactions. We propose a graph-based approach to generate various types of association rules from a large database of customer transactions. This approach scans the database once to construct an association graph and then traverses the graph to generate all large item sets. Empirical evaluations show that our algorithms outperform other algorithms which need to make multiple passes over the database.