Towards ubiquitous mining of distributed data

被引：0

作者：

Kargupta, H ^{[1
]}

Sivakumar, K ^{[1
]}

Huang, WY ^{[1
]}

Ayyagari, R ^{[1
]}

Chen, R ^{[1
]}

Park, BH ^{[1
]}

Johnson, E ^{[1
]}

机构：

[1] Univ Maryland Baltimore Cty, Dept Comp Sci & Elect Engn, Baltimore, MD 21250 USA

来源：

DATA MINING FOR SCIENTIFIC AND ENGINEERING APPLICATIONS | 2001年 / 2卷

关键词：

distributed data mining; principal component analysis; clustering; decision trees; Bayesian networks; multivariate regression;

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：

The demand for understanding and exploring large quantity of data is growing fast in many domains. Scientific research is one among them. While the role of high performance computers in scientific data analysis is important, networks of workstations and the so called "thin" computing devices like the laptops, palmtops, and wearable computers are playing increasingly important roles in this domain. This chapter presents an overview of a collection of techniques that are designed for analyzing heterogeneous data distributed over a network of different computing and storage devices. The collective data mining approach presented here, pays careful attention to the overhead of data communication in a heterogeneous network and offers the capability of ubiquitous mining from distributed data.

引用

页码：281 / 306

页数：26

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