Synthesizing algorithm for mining composite-frequent item sets
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摘要: 关联规则挖掘的关键在于频繁项目集的求解,为了能够在含有数值类型数据的交易数据库中快速求解含有多值的频繁项目集,拓展了含有多种数值的交易数据库定义.在此基础上,根据树的思想,建立含有交易项和交易数量的树,并结合Apriori算法和智能搜索,提出在各个较小的树枝路径中求解频繁项目集求解方法FABCTA(Fast Algorithm ByCandidate Transaction Tree and Apriori).通过采用真实数据实验对比,FABCTA效率明显优于Apriori算法.Abstract: It is very important to get the frequent item set in the associate rule mining. In order to fast obtain the frequent item set from a database that includes multiple values, the definition of transaction database was extended. And then by the tree concept, a special tree was built in which every node is formed by item and item’s count. At last, on the foundation of Apriori Algorithm and Artificial Intelligent Search, FABCTA(fast algorithm by candidate transaction tree and apriori) was presented to solve the frequent item set in small branches of tree. By the test on real data, FABCTA is more efficient than Apriori algorithm.
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Key words:
- databases /
- trees /
- rules /
- search theory /
- Apriori algorithm
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