MIDBSCAN: An Efficient Density-Based Clustering Algorithm

被引：0

作者：

Tsai, Cheng-Fa ^{[1
]}

Sung, Chun-Yi ^{[1
]}

机构：

[1] Natl Pingtung Univ Sci & Technol, Dept Management Informat Syst, Pingtung 91201, Taiwan

来源：

SIXTH INTERNATIONAL SYMPOSIUM ON NEURAL NETWORKS (ISNN 2009) | 2009年 / 56卷

关键词：

Data mining; Data clustering; Density-based clustering algorithm;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This investigation presents a clustering algorithm that incorporates neighbor searching into the density-based IDBSCAN algorithm. The rooted algorithm performs fewer searches than standard IDBSCAN. Experimental results indicate that the proposed MIDBSCAN algorithm has a lower execution time cost than DBSCAN, IDBSCAN or KIDBSCAN. MIDBSCAN has a maximum deviation in clustering correctness rate of 0.1%, and a maximum deviation in noise data filtering rate of 0.3%.

引用

页码：469 / 479

页数：11

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