Data mining techniques to study voting patterns in the US

被引：1

作者：

Bagui, Sikha ^{[1
]}

Mink, Dustin ^{[1
]}

Cash, Patrick ^{[1
]}

机构：

[1] Department of Computer Science, University of West Florida, Pensacola

来源：

Data Science Journal | 2007年 / 6卷

关键词：

Association rule mining; Attribute relevance study; Data mining; Data preprocessing; Decision tree analysis; Voting patterns;

D O I：

10.2481/dsj.6.46

中图分类号：

学科分类号：

摘要：

This paper presents data mining techniques that can be used to study voting patterns in the United States House of Representatives and shows how the results can be interpreted. We processed the raw data available at http://clerk.house.gov, performed t-weight calculations, an attribute relevance study, association rule mining, and decision tree analysis and present and interpret interesting results. WEKA and SQL Server 2005 were used for mining association rules and decision tree analysis.

引用

页码：46 / 63

页数：17

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