Journal of Frontiers of Computer Science and Technology ›› 2011, Vol. 5 ›› Issue (12): 1121-1130.

• 学术研究 • Previous Articles     Next Articles

Accurate and Efficient Dynamic Interventions Discovery

ZHU Xiangyu, HU Tianran, ZHANG Yue, LI Chuan, TANG Changjie, ZHU Jun   

  1. 1. School of Computer Science, Sichuan University, Chengdu 610065, China
    2. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China 3. National Birth Defects Monitoring Center, Chengdu 610065, China

  • Received:1900-01-01 Revised:1900-01-01 Online:2011-12-01 Published:2011-12-01


朱翔昱, 胡天冉, 张 悦, 李 川, 唐常杰, 朱 军   

  1. 1. 四川大学 计算机学院, 成都 610065
    2. 香港科学与技术大学 计算机科学及工程学系, 香港特别行政区
    3. 中国出生缺陷监测中心, 成都 610065


Intervention decision is the hot topic concerned by data mining fields, which wants to evaluate the influence of intervention methods to intervention targets or discover the most effective method to achieve intervention targets, but naive intervention rule (NIR) does not express interventional knowledge accurately and efficiently due to its simplicity. This paper introduces the idea of Markov chains in intervention model which substantially improves the evaluation accuracy and accelerates the mining process, proposes a dynamic model of intervention process, and designs and realizes an accurate evaluating system of intervention process based on intervention intensity. Experi-mental results in the dataset of Chinese Birth Defects show the effectiveness of the proposed method.

Key words: intervention intensity, Markov chains, dynamic intervention model

摘要: 干预决策是数据挖掘领域关注的重要问题, 致力于评价干预措施对干预目标的影响或发现满足干预目标的最优干预措施, 而朴素干预规则模型简单, 无法精确表达干预知识, 且效率较差。在模型设计中引入了马尔科夫链, 提出了干预过程动态模型, 设计并实现了基于干预力度的动态精确干预评价体系。在中国出生缺陷数据集上的实验表明, 该方法可比较精确地发现干预规则。

关键词: 干预力度, 马尔科夫链, 动态干预模型