时态数据挖掘的相似性发现技术  

Similarity Discovery Techniques in Temporal Data Mining

作　　者：潘定[1] 沈钧毅[2] 

机构地区：[1]暨南大学管理学院,广东广州510632 [2]西安交通大学计算机科学与技术系,陕西西安710049

出　　处：《软件学报》2007年第2期246-258,共13页Journal of Software

基　　金：国家自然科学基金Nos.60173058;70372024~~

摘　　要：现实世界存在着大量的时态数据,时态数据挖掘(temporal data mining,简称TDM)是近年来学术界关注的一个重要研究课题.相似性发现技术关注数据的发展变化,试图从时态数据中发现事物动态演化的相似性规律.分析和比较了近年来TDM研究中涉及的主要相似性发现技术.首先区分定义了3类时态数据:时间序列、事件序列和交易序列;然后分类并讨论了各种与序列相关的主要方法和技术,涉及相似性度量、序列抽象表示和搜索,以及各类挖掘任务及其算法操作;最后展望进一步研究的方向.Temporal data mining （TDM） has been attracting more and more interest from a vast range of domains, from engineering to finance. Similarity discovery technique concentrates on the evolution and development of data, attempting to discover the similarity regularity of dynamic data evolution. The most significant techniques developed in recent researches to deal with similarity discovery in TDM are analyzed. Firstly, the definitions of three categories of temporal data, time series, event sequence, and transaction sequence are presented, and then the current techniques and methods related to various sequences with similarity measures, representations, searching, and various mining tasks getting involved are classified and discussed. Finally, some future research trends on this area are discussed.

关 键 词：数据挖掘 时态数据 相似性发现 时态规则 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]