RETRACTED: Survey of Clustering and Outlier Detection Techniques in Data Mining: A Research Perspective (Retracted Article)

被引：0

作者：

Devi, R. Delshi Howsalya ^{[1
]}

Devi, M. Indra ^{[2
]}

机构：

[1] KLN Coll Engn, Madurai, Tamil Nadu, India

[2] Ultra Coll Engn, HOD, Madurai, Tamil Nadu, India

来源：

ADVANCEMENTS IN AUTOMATION AND CONTROL TECHNOLOGIES | 2014年 / 573卷

关键词：

Outlier; Distance-based; Density-Based;

D O I：

10.4028/www.scientific.net/AMM.573.511

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

The Outlier detection is one of the major issues that has been worked out deeply within the Data Mining domain. It has been used to detect dissimilar observations within the data taken into the account. Detection of outliers helps to recognize the system faults and thereby helping the administrators to take preventive measures before it rises. In this paper, we recommends a comprehensive survey of an outlier detection. We anticipate this survey will support a better understanding of various directions in which experimental approach can be done on this topic.

引用

页码：511 / +

页数：4

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