基于规则的分类数据离群挖掘方法研究

被引:22
作者
史东辉
蔡庆生
倪志伟
张春阳
机构
[1] 中国科学技术大学计算机科学系!合肥
关键词
规则; 离群数据; 离群挖掘; 分类数据;
D O I
暂无
中图分类号
TP311 [程序设计、软件工程];
学科分类号
摘要
离群数据的挖掘 (outlier mining,简称离群挖掘 )是数据挖掘的重要内容 ,现有的离群数据挖掘算法大多对分类数据 (categorical data)缺乏有效的处理 ,提出了基于规则的分类数据离群挖掘方法 ,采用多层最大离群支持度 maxsup,搜索离群规则 ,有效地解决了这一问题 ,用这一方法对医学流行病数据进行了各种实验 ,分析了该方法的适用范围、性能 ,验证了方法正确性 ;另外 ,实验表明 ,经过离散化后 ,基于规则的分类数据离群挖掘算法对连续性属性的数据也是有效的 .
引用
收藏
页码:1094 / 1100
页数:7
相关论文
共 9 条
  • [1] Algorithms for mining distance-based outliers in large datasets.In: Proc of the 24th VLDB Conf. Knorr E,Ng R. New York . 1998
  • [2] Mining association rules between sets of items in large databases.In: Proc of ACM SIGMOD Conf on Management of Data(AIGMOD’ 93). Agrawal R,Imielinski T,Swami A. Washington . 1993
  • [3] Rock: A robust clustering algorithm for categorical attributes.In: Proc of 1999 Int’ l Conf on Data Engineering. Guha S,Rastogi R,Shim K. Sydney . 1999
  • [4] A linear method for deviation in large database.In: Proc of Int’ l Conf on Data Mining and Knowledge Discovery(KDD’ 96). Arning A,Agrawal R,Raghavan P. Portland . 1996
  • [5] Fast algorithms for mining association rules. R.Agrawl,R.Srikant. proc. Of 20th Int‘l Conf on Very Large Databases (VLDB‘94) . 1994
  • [6] Finding Intensional Knowledge of Distance-based Outliers. E. Knorr.and R.Ng. . 1999
  • [7] Outliers in Statistical Data. Barnett V,Lewis T. New York: JohnWiley &Sons . 1994
  • [8] OPTICS-OF: Identifying local outliers.In: Proc of the 3rd European Conf on Principles and Practice of Knowledge Discovery in Databases(PKDD’ 99). Breuning M,Kriegel H,Ng R. Prague . 1999
  • [9] A unified approach formining outliers:Properties and computation. Knorr E,Ng R. Proc of1997Int’’ l Conf on Knowledge Discoveryand Data Mining ( KDD’’ 97) . 1997