QUERY OPTIMIZATION TECHNIQUE FOR DATA MINING

我们研究了如何根据常见的统计度量（例如卡方值或相关系数）有效地计算显着关联规则。为此，可以考虑使用Apriori算法，但该算法需要进行重大转换，因为这些统计指标都不是反单调的，并且使用更高的支持来减少搜索空间并不能保证其搜索空间中的解决方案。我们开发了一种估计与项集的任何超集相关的统计度量的严格上限的方法，以及新颖地使用上限的结果信息来在遍历项集格时修剪无效的超集。实验测试证明了该方法的有效性。

We have studied how to efficiently compute significant association rules according to common statistical measures such as a chi-squared value or correlation coefficient. For this purpose, one might consider to use of the Apriori algorithm, but the algorithm needs major conversion, because none of these statistical metrics are anti-monotone, and the use of higher support for reducing the search space cannot guarantee solutions in its the search space. We have developed a method of estimating a tight upper bound on the statistical metric associated with any superset of an itemset, as well as the novel use of the resulting information of upper bounds to prune unproductive supersets while traversing itemset lattices. Experimental tests demonstrated the efficiency of this method.