Abstract: During the process of mining quantitative association rules， the semantics may be lost due to the discretization of quantitative values. To avoid the loss of semantic information， a novel algorithm， MPSQAR （mining preserving semantic quantitative association rule）， is proposed to handle the quantitative association rules mining. The main contributions include： （1） Propose a new method to normalize the quantitative values； （2） Propose a method to assign a weight for each attribute to reflect the values distribution； （3） Extend the weight-based association model to tackle the quantitative values in association rules without partition； （4） Design a integrated and uniform method to mine the traditional Boolean association rules and quantitative association rules； Experiments show the effectiveness and linear scalability of the new method on time consuming.

Key words: quantitative association rule, MPSQAR algorithm, loss of semantic information

摘要： 在挖掘量化关联规则的过程中，由于对量化值的划分，将产生语义损失。为避免这种情况，提出基于无损语义的算法MPSQAR来处理量化关联规则的挖掘。主要工作包括：（1）提出规泛化量化值的新方法；（2）提出反映属性值分布的属性权重设计方法；（3）扩展加权关联规则模型以处理量化关联规则，避免量化值的划分；（4）提出挖掘传统布尔关联规则和量化关联规则的集成方法；实验表明算法MPSQAR的有效性和时间消耗随时间趋势呈线性增长。

关键词: 量化关联规则, MPSQAR算法, 语义信息损失