Efficient disk-based K-means clustering for relational databases

被引：50
|
作者：
Ordonez, C
Omiecinski, E
机构：
[1] Teradata, Rancho Bernardo, CA 92127 USA
[2] Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA
来源：
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | 2004年 / 16卷 / 08期
关键词：
clustering; K-means; relational databases; disk;
D O I：
10.1109/TKDE.2004.25
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
K-means is one of the most popular clustering algorithms. This article introduces an efficient disk-based implementation of K-means. The proposed algorithm is designed to work inside a relational database management system. It can cluster large data sets having very high dimensionality. In general, it only requires three scans over the data set. It is optimized to perform heavy disk I/O and its memory requirements are low. Its parameters are easy to set. An extensive experimental section evaluates quality of results and performance. The proposed algorithm is compared against the Standard K-means algorithm as well as the Scalable K-means algorithm.
引用
收藏
页码：909 / 921
页数：13
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