Mining quantitative association rules is an important task of data mining.
量化关联规则的挖掘是数据挖掘的一项重要任务。
This paper presents a cluster method for discretization in the processing of mining quantitative association rules.
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。
Based on the theory of quantitative association rules, the numeric data is divided into intervals with statistical method.
本文基于数值型关联规则的理论，用一种数理统计的方法进行连续属性的离散化。
A novel algorithm, QAR, for mining quantitative association rules and an incremental updating algorithm, IUQAR, are proposed.
提出了一种新的量化关联规则挖掘算法QAR及其增量式更新算法IUQAR。
Discovering association rules is an important data mining problem. While discovering Quantitative association rules differs from traditional Boolean 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。
Based on the summarization of data mining research, the problem of mining inter-transactional quantitative association rules are brought forward and defined in this paper.
在归纳现有关联规则研究的基础上提出了事务间数值型关联规则的数据挖掘问题，并对该问题进行了定义。
Most quantitative association rules transform mining association rules of numeric property into boolean property, and the kernel problem is to divide the numeric data into intervals.
数值型关联规则的算法大多是将多值属性关联规则挖掘问题转化为布尔型关联规则挖掘问题，而连续属性的离散化是数值型关联规则的核心问题。
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.
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。
Attributes in the database of tumor diagnoses are usually quantitative attributes, so quantitative attribute discretization is a problem of mining association rules.
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。
Based on the above problem this paper presented a method called association rules mining with fuzzy quantitative constraints.
基于上述问题，提出了一个基于模糊数值约束的关联规则挖掘方法，实际挖掘结果表明这种方法是有效的。
Based on the above problem this paper presented a method called association rules mining with fuzzy quantitative constraints.
基于上述问题，提出了一个基于模糊数值约束的关联规则挖掘方法，实际挖掘结果表明这种方法是有效的。
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