山东理工大学学报（自然科学版）2016，Vol.30Issue(6)：7-11,5.
基于密度的局部离群数据挖掘算法研究
Study of local outliers mining algorithm based on density
摘要
Abstract
In order to study the outliers mining algorithm,we combined K-means algorithm with influenced local outlier factor (referred to as INFLOF)algorithm,and generated an algorithm which based on the K-means and influenced local outlier factor (referred to as K-INFLOF). Firstly,this method removed normal data from the dense areas near the center of the class,then called INFLOF algorithm excavate remaining data,thereby reducing storage of the intermediate results,greatly reduced the running time of the algorithm.Finally the accuracy and efficiency of K-INFLOF algorithm in data mining is verified by random data and real data experiments respec-tively.关键词
离群数据挖掘/INFLOF算法/K-means算法/时间复杂度Key words
outiliers detection/INFLOF (influenced local outlier factor) algorithm/K-means algorithm/time complexity分类
信息技术与安全科学引用本文复制引用
许琳,赵茂先..基于密度的局部离群数据挖掘算法研究[J].山东理工大学学报（自然科学版）,2016,30(6):7-11,5.基金项目
国家自然科学基金项目(61572128) （61572128）
