它所包含的模式数量比频繁集所包含的模式数量小若干数量级。
It has smaller orders of magnitude than the set of all frequent itemsets.
分析结果表明，利用规则兴趣度能够大大减小候选项目集的大小，有效提高频繁模式挖掘算法的效率。
The result indicates that we can remarkably decrease the candidate items and improve the efficiency of mining frequent pattern when using the interest measure.
该算法通过对模式树的各种操作简化了对频繁项集的搜索过程。
To make further improvement on the scalability of the algorithm, we make a further study on the pattern tree, and propose a new algorithm called FP-DFS based on the study.
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