摘要
对比挖掘是近年来数据挖掘领域的新热点之一。对比挖掘关注并描述不同类别和条件下,或随时间变化的知识,旨在设计能够发现刻画数据集中不同类别或条件的样本间差异的模式或模型的方法。由于对比挖掘技术能化繁为简、准确分类,在实践中得到广泛应用。显露模式的挖掘和应用是对比挖掘的重要分支。综述了显露模式的背景、基本概念和原理,分析了显露模式的挖掘方法,讨论了显露模式的扩展定义和挖掘,介绍了基于显露模式的分类器构造方法,展示了显露模式的若干实际应用,展望了基于显露模式的对比挖掘的未来研究。
Contrast mining is one of fairly new hot data mining topics. Contrast mining focuses on knowledge that describes differences between classes and conditions, or describes changes over time. Contrast mining aims at developing techniques to discover patterns or models that contrast, and characterize multiple datasetsassoeiated with different classes or conditions. Contrast mining has wide applications in reality, due to its ability of simplifying problems and classifying accurately. Research on the mining and application of emerging patterns represents a major direction of contrast mining. This paper provided a survey of such issue. More specifically, after introducing the background, basic concepts and principles of emerging patterns, the paper analyzed the mining methods of emerging patterns, discussed extended definitions of emerging patterns and their mining, stated methods for constructing emerging pattern based classifiers, and illustrated applications of emerging pattern in several real-world fields. Finally, this paper gave out some topics for future research on emerging pattern based contrast mining.
出处
《计算机应用》
CSCD
北大核心
2012年第2期304-308,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(61103042,61173099)
高等学校博士学科点专项科研基金资助项目(20100181120029)
四川大学青年教师科研启动基金资助项目(2009SCU11030)
关键词
数据挖掘
显露模式
模式发现
频繁项集
分类
data mining
emerging pattern
pattern discovery
frequent itemset
classification
