Outliers detection method based on K-means and agglomerative clustering

doi:10.3778/j.issn.1002-8331.2009.29.039

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (29): 131-133.DOI: 10.3778/j.issn.1002-8331.2009.29.039

• 数据库、信号与信息处理 • Previous Articles Next Articles

Outliers detection method based on K-means and agglomerative clustering

ZENG Ying，LUO Ke，ZOU Rui-zhi

College of Computer and Communication Engineering，Changsha University of Science and Technology，Changsha 410076，China

Received:2008-06-02 Revised:2009-04-22 Online:2009-10-11 Published:2009-10-11
Contact: ZENG Ying

基于K-均值聚类和凝聚聚类的离群点查找方法

曾颖，罗可，邹瑞芝

长沙理工大学计算机与通信工程学院，长沙 410076

通讯作者: 曾颖

Abstract

Abstract: Outliers detection is an important issue in data mining.In this paper，according to the characteristics of data streams，an outliers detection method based on k-means and agglomerative clustering is proposed，which uses k-means clustering to find some intermediate results，and applies agglomerative clustering on each intermediate result to pick out potential outliers.

Key words: data mining, outliers, k-means clustering, agglomerative clustering

摘要： 离群点发现是数据挖掘研究的一个重要方面。根据数据流的特点，给出了一种基于K-均值聚类和凝聚聚类的离群点发现方法，先用K-均值聚类对数据流进行处理，生成中间聚类结果，然后用凝聚聚类对这些中间结果进行再次选择，最后找出可能存在的离群点。

关键词: 数据挖掘, 离群点, K-均值聚类, 凝聚聚类

CLC Number:

TP311

ZENG Ying，LUO Ke，ZOU Rui-zhi. Outliers detection method based on K-means and agglomerative clustering[J]. Computer Engineering and Applications, 2009, 45(29): 131-133.

曾颖，罗可，邹瑞芝. 基于K-均值聚类和凝聚聚类的离群点查找方法[J]. 计算机工程与应用, 2009, 45(29): 131-133.

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Outliers detection method based on K-means and agglomerative clustering

基于K-均值聚类和凝聚聚类的离群点查找方法

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