Journal of Frontiers of Computer Science and Technology ›› 2009, Vol. 3 ›› Issue (4): 392-404.DOI: 10.3778/j.issn.1673-9418.2009.04.006

• 学术研究 • Previous Articles     Next Articles

MPSQAR: Mining Quantitative Association Rules without Loss of Semantics

ZENG Chunqiu+, TANG Changjie, LI Chuan, DUAN Lei   

  1. School of Computer Science, Sichuan University, Chengdu 610065, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-07-15 Published:2009-07-15
  • Contact: ZENG Chunqiu

MPSQAR:无损语义的量化关联规则挖掘算法

曾春秋+,唐常杰,李 川,段 磊   

  1. 四川大学 计算机学院,成都 610065
  • 通讯作者: 曾春秋

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