期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于聚类的离群数据挖掘及应用 被引量：2

Outliers Mining and Application　Based on Clustering
下载PDF
导出
摘要 介绍了离群数据挖掘的基本概念,全面分析并总结了离群数据挖掘研究的历史与现状,以及离群数据挖掘的几类方法,并对一些典型方法进行了分析和评价,指出传统方法的优点和不足,展望了今后的研究工作。 The basic concept about outliers mining is introduced in this paper. After having reviewed and analyzed the history and current status of research on outliers mining, some typical algorithms are analyzed, the weakness of the traditional measure method are pointed out. And the future of the research is forecast.
作者 蔡江辉 张继福
机构地区 太原科技大学计算机科学与技术学院
出处 《太原重型机械学院学报》 2004年第4期254-258,共5页 Journal of Taiyuan Heavy Machinery Institute
基金 国家"863"高技术研究发展计划基金项目资助(2003AA133060)
关键词 离群数据挖掘 聚类 优点 全面分析 展望 研究工作 基本概念 outliers mining, clustering, association rule
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论] F713.3 [经济管理—产业经济]
  • 相关文献

参考文献15

  • 1Knorr E, Ng R. Algorithms for mining distance-based outliers in large datasets [A]. Proc. of the 24 VLDB Conference[C]. New York, USA,1998.392-403.
  • 2Zhang T, Ramakrishman R, Livny M. BIRCH:An efficient data clustering methodfor very large databases[A].In Proc.1996 ACM-SIGMOD Int.Conf Management of Data(SIGMOD'96)[C]. Canada,1996, 103-114.
  • 3Hinneburg H,Keim D A. An efficient approach to clustering in large multimedia databases with noise[C]. 1998 Int Conf. Knowledge Discovery and Data Mining (KDD'98)[C].New York:1998,58-65.
  • 4Ester M, Kriegel H. -P, Xu X. Knowledge discovery in large spatial databases: Focusing techniques for efficient class identification[A]. 4th Int. Symp. Large Spatial Databases (SSD'95)[C].Portland,ME, 1995,67-82.
  • 5Ankerst M,Breunig M,Kriegel H-P,Sander J. OPTICS: Ordering points to identify the clustering structure[A]. 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD'99)[C]. ,Philadelphia,PA,1999,49-60.
  • 6Han J, Kamber M. Data Mining: Concepts and Techniques[A].Copyright by Morgan Kaufmann Publishers[C], Inc,2001.
  • 7Breuning M,Kricgel H,Ng R.OPTICS-OF: Identifying Local Outliers[A]. In: Proc.of the 3rd European conference on Principles and Practice of knowledge Discovery in Databases(PKDD'99)[C].Prague,1999.262-270.
  • 8Arning A, Agrawal R,Raghavan P . A linear method for deviation in large database [A].In:Proc. of Int. Conf. Data Mining and Knowledge Discovery(KDD96)[C]. Portland, 1996,164-169.
  • 9Knorr E,Ng R. A Unified approach for mining outliers: properties and computation [A]. Proc. of 1997 IntConf Knowledge Discovery and Data Mining(KDD'97)[C]. Newport Beach, California, 1997. 219-222.
  • 10Knorr E,Ng R. Finding in tensional knowledge of distance-based [A]. Proc. of the 25th VLDB Conference Edinburgh[C]. Scotland, 1999. 211-222.

同被引文献19

  • 1迟庆云.决策树技术在汽车销售中的应用[J].微计算机信息,2008,24(9):145-146. 被引量：3
  • 2俞琳琳,吉根林.离群数据挖掘方法研究[J].信息技术,2005,29(11):86-89. 被引量：1
  • 3KNNOR E, NG R. Algorithms for mining distance-based outliers in large datasets [ C ]//Proc of the 24th VLDB Conference. New York : Morgan Kaufmann, 1998 : 392-403.
  • 4HAN J W, KAMBER M. Data Mining Concepts and Techniques[ M ]. San Francisco:Morgan Kaufmann publishers,2001.
  • 5BARNETr V, LEWIS T. Outliers in statistical data[ M ]. New York :John Wiley and Sons, 1994.
  • 6PREPARATA F, SHAMOS M. Computational Geometry : An Introduction [ M ]. USA : Springer-Verlag, 1988.
  • 7SARAWAGI S, AGRNWAL K, MEGIDDO N. Discovery-driven Exploration of OLAP Data Cubes [ C ]//Valencia: Proc of Int Conf Extending Database Technology, 1998 : 168-182.
  • 8BREUNIG M, KRIGEGL H P, NG R T, SANDER J. LOF: Identifying density-based local outlier[ C ]//Zytkow J Med. Ranch Proc of the 3rd European Conference on Principles and Practice of knowledge Discovery in Databases. Prague:Springer, 1999:262-270.
  • 9AGARARWAL, YU P S. An effective and efficient algorithm for high-dimensional outlier detection [ J ]. The International Journal on Very Large Data Bases ,2005,14 ( 2 ) :211-221.
  • 10ZHU C, KITAGAWA K, FALOUTSOS C. Example-Based Robust Outlier Detection in High Dimensional Datasets [ C ]//ICDM' 05,2005:829-832.

引证文献2

二级引证文献3

太原重型机械学院学报
2004年 第4期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部