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Research on improved Data-Mining Algorithm based on Strong Correlation

机译:基于强相关性的改进数据挖掘算法研究

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The extensive application of association rules in commerce enables itself to be one of the most active research directions in data mining. Recently, the mining of strong correlation item pairs with statistical significance in transaction database receives a certain value. In order to further reduce the cost of testing candidate item pairs in relational database, we have developed the Taper algorithm according to 1NF property. The developed TaperR algorithm can cut the number of candidate pairs to improve efficiency.
机译:商业协会规则的广泛应用使自己成为数据挖掘中最活跃的研究方向之一。最近,在交易数据库中具有统计显着性的强相关项对的挖掘接收了一定的价值。为了进一步降低关系数据库中的测试候选物品对的成本,我们开发了根据1NF属性的锥形算法。开发的Taperr算法可以减少候选对的数量以提高效率。

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