郑州轻工业学院学报（自然科学版）2011，Vol.26Issue(3)：8-11,4.
基于聚类融合的异常检测算法
An outlier detection algorithm based on clustering ensemble
摘要
Abstract
An outlier mining algorithm based on the clustering ensemble was presented in order to reduce the reliance for users and decrease the high false positive rate due to taking the small size clusters as the outliers directly. Outliers can be found according to the abnormal frequency of every record. The algorithm is able to provide the user a more friendly operation. The experimental results on the real-life datasets showed that the proposed algorithms are feasible and effective comparing with other classical algorithms and can be used for mixed dataset.关键词
异常检测/聚类融合/异常簇/任意形状聚类Key words
outlier detection/clustering ensemble/abnormal cluster/arbitrary shape clustering分类
信息技术与安全科学引用本文复制引用
苏晓珂,王秉政..基于聚类融合的异常检测算法[J].郑州轻工业学院学报（自然科学版）,2011,26(3):8-11,4.基金项目
河南省科技攻关项目(092102210108) （092102210108）
河南省教育厅自然科学基础研究计划项目(2010A520033) （2010A520033）
郑州轻工业学院博士科研基金资助项目 （）
