The rules mined by the quantitative association rules algorithms based on the trapezium梒loud model is effective and understandable, so we can find out more meaningful knowledge from the data.
在此基础上给出的数量型挖掘算法得到的数值型关联规则具有有效性和可理解性，能够从数据中挖掘出更有意义的知识。
参考来源 - 基于梯形云模型的数量型关联规则挖掘方法The critical of quantitative association rules problem to boolean association rules problem changing is how to divide the sections.
如何划分区段是实现多值属性关联规则问题到布尔型关联规则问题转变的关键。
参考来源 - 多值属性关联规则的研究与实现·2,447,543篇论文数据，部分数据来源于NoteExpress
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。
应用推荐