Implementation issues in the design of I/O intensive Data Mining applications on clusters of workstations

被引：0
|
作者：
Baraglia, R [1 ]
Laforenza, D
Orlando, S
Palmerini, P
Perego, R
机构：
[1] CNR, Ist Cnuce, I-56100 Pisa, Italy
[2] Univ Ca Foscari Venezia, Dipartimento Informat, Venice, Italy
来源：
PARALLEL AND DISTRIBUTED PROCESSING, PROCEEDINGS | 2000年 / 1800卷
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper investigates scalable implementations of out-of-core I/O-intensive Data Mining algorithms on affordable parallel architectures, such as clusters of workstations. In order to validate our approach, the K-means algorithm, a well known DM Clustering algorithm, was used as a test case.
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收藏
页码：350 / 357
页数：8
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