应用聚类方法研究了数量关联规则提取过程中的连续属性离散化问题。
This paper presents a cluster method for discretization in the processing of mining quantitative association rules.
关联规则的发现是数据挖掘的一个重要方面，而数量关联规则的发现不同于传统的布尔型关联规则。
Discovering association rules is an important data mining problem. While discovering Quantitative association rules differs from traditional Boolean association rules.
当您展开这棵树时，每一个规则类别和规则都显示出生成结果的数量，并且列出和该规则相关联的资源(请参见图17所示)。
When you expand the tree, each rule category and rule displays the number of results generated and lists the resources that are associated with the rule (see Figure 17).
对数量型属性，应用竞争聚集算法将数量型属性划分成若干个模糊集，并系统地提出加权模糊关联规则的挖掘算法。
As for quantitative attributes, they are divided into several fuzzy sets by the competitive agglomeration algorithm, and then the algorithm for mining weighted fuzzy association rules is provided.
肿瘤诊断数据库中的属性常为数量型属性，因此如何将数量型属性离散化是挖掘关联规则的难点。
Attributes in the database of tumor diagnoses are usually quantitative attributes, so quantitative attribute discretization is a problem of mining association rules.
针对关联规则数量巨大并且存在极大冗余的问题，提出无冗余告警关联规则产生算法。
Non-redundant association rules mining algorithm is proposed to deal with the problem of huge rules' number and redundancy.
因此，在关联规则发现中引入销售数量的利润约束问题显得很必要。
Therefore, it is necessary apparently that profit constraint in association rules mining has been induced sales.
在以前的研究中，关联规则发现算法一般都没有考虑项目的销售数量。
In previous work, items sales had not been considered in the algorithm discovery association rules.
该方法不仅能够减少关联规则数量，而且不会带来规则丢失。
该方法不仅能够减少关联规则数量，而且不会带来规则丢失。
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