Applied Comparison of DBSCAN, OPTICS and K-Means Clustering Algorithms

被引：0

作者：

Bilgin, Turgay Tugay ^{[1
]}

Camurcu, Yilmaz ^{[2
]}

机构：

[1] Maltepe Univ, Muhendislik Mimarlik Fak, Basibuyuk Kampusu, Istanbul, Turkey

[2] Marmara Univ, Tekn Egitim Fak, Istanbul, Turkey

来源：

JOURNAL OF POLYTECHNIC-POLITEKNIK DERGISI | 2005年 / 8卷 / 02期

关键词：

Data mining; Clustering Analysis; DBSCAN; OPTICS; K-means;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

DBSCAN and OPTICS are two recent clustering algorithms on data mining. In this study, these two algorithms and K-means which is one of the oldest clustering algorithms are compared. Comparison is based on cluster discovery performance on synthetic database. Consequently, two recent clustering algorithms DBSCAN and OPTICS are performed superior accuracy and cluster discovery ability over K-means algorithm.

引用

页码：139 / 145

页数：7

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