Novel Outlier Detection by Integration of Clustering and Classification

被引：0
|
作者：
Tripathy, Sarita [1 ]
Sahoo, Laxman [1 ]
机构：
[1] KIIT Univ, Sch Comp Engn, Bhubaneswar, Odisha, India
来源：
DATA SCIENCE AND BIG DATA ANALYTICS | 2019年 / 16卷
关键词：
DBSCAN; Decision; Tree; Clustering; Outlier detection; Classification;
D O I：
10.1007/978-981-10-7641-1_14
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
A unique method of outlier detection consisting of integration of clustering and classification is proposed here. Basically the algorithm is divided into two parts the first phase consists of application of the classical DBSCAN algorithm to the data set which is followed by the second phase which consists of application of decision tree classification algorithm. The analysis on the algorithm states that the accuracy of unwanted data detection is high in the proposed method.
引用
收藏
页码：169 / 176
页数：8
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