文章基本信息

标题：Enhanced DBSCAN Outlier Detection
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作者：Priyamvada Paliwal ; Meghna Sharma
期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：3
出版社：S.S. Mishra
摘要：Most real-world databases include a certain amount of exceptional values, generally termed as "outliers". The DBSCAN algorithm can identify clusters in large spatial data sets by looking at the local density of database elements, using only one input parameter. This paper presents a comprehensive study of Outlier Detection and DBSCAN , algorithm The salient of this paper to present enhanced DBSCAN algorithm with its implementation with the complexity. And there are also additional features described with this algorithm for finding outliers
关键词：C Density-based sp atial clustering of applications with noise; Threshold Distance; Minimum Points; Cluster; ;Density.