实时数据流聚类的研究新进展  

Research and advances of real-time data stream clustering

作　　者：张晓龙[1] 曾伟[1] 

机构地区：[1]武汉科技大学计算机科学与技术学院,湖北武汉430065

出　　处：《计算机工程与设计》2009年第9期2177-2181,2186,共6页Computer Engineering and Design

基　　金：国家自然科学基金项目(60674115)

摘　　要：实时数据流聚类是目前国际数据库和数据管理领域的新兴研究热点。综述了实时数据流聚类的最新研究进展,在介绍实时数据流聚类的相关理论和常用技术的基础上,对现有各种代表性算法的优势和不足进行了系统地分析,从处理速度、聚类形状、演化分析、高维性及噪声健壮性5个方面对算法的性能进行了比较。探讨了基于聚类的实时数据流演化分析方法及其局限性。最后展望了将来可能的研究方向。Real-time data stream clustering is an international novel research hotspot in the field of database and data management.The newest development of real-time data stream clustering research is overviewed.Interrelated theory and common techniques of real-time data stream clustering is introduced firstly.Then strengths and weaknesses of different-kind representational algorithms are analyzed systematically.Performances of these algorithms are compared subsequently in five aspects including execution speed,shape of cluster,evolving analysis,high-dimension and haleness of noise.Data stream evolving analysis based on clustering and its limitations are presented.Finally,the future possible research issues are discussed.

关 键 词：实时数据流 聚类 演化分析 数据流挖掘 混合属性 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]