一种改进的基于密度的离群数据挖掘算法  

An improved density-based outlier mining algorithm

作　　者：崔贯勋[1,2] 朱庆生[1] 

机构地区：[1]重庆大学计算机学院,重庆400044 [2]重庆工学院计算机科学与工程学院

出　　处：《计算机应用》2007年第3期559-560,573,共3页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60403009);重庆市自然科学基金资助项目(2005BB2224)

摘　　要：利用基于密度的离群数据挖掘算法离群数据不在非离群数据指定的邻域内的特点,改进了原有的离群数据挖掘算法:首先判断数据是否在某个非离群数据指定的邻域内,如果不在,再判断其邻域内数据的个数。通过对二维空间数据测试表明,改进的算法能够快速有效地挖掘出数据集中的离群数据,速度上数倍于原来的算法。Based on the characteristic that outliers are not included in the appointed neighborhood of inliers, an improved algorithm for outlier mining was proposed. Data was judged whether it was included in the appointed neighborhood of inliers firstly. If the answer was negative, the number of data that was included in the appointed neighborhood was counted. Experimental results show that the improved algorithm is effective and efficient in outlier mining and it is faster than the original algorithm.

关 键 词：数据挖掘 离群数据 基于密度 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]