Finding "persistent rules": Combining association and classification results

被引：8

作者：

Rajasethupathy, Karthik ^{[2
]}

Scime, Anthony ^{[1
]}

Rajasethupathy, Kulathur S. ^{[1
]}

Murray, Gregg R. ^{[3
]}

机构：

[1] SUNY Coll Brockport, Dept Comp Sci, Brockport, NY 14420 USA

[2] Cornell Univ, Dept Math, Ithaca, NY 14853 USA

[3] Texas Tech Univ, Dept Polit Sci, Lubbock, TX 79409 USA

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 03期

关键词：

Association mining; Classification; Persistent rules; Strong rules;

D O I：

10.1016/j.eswa.2008.06.090

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Different data mining algorithms applied to the same data can result in similar findings, typically in the form of rules. These similarities can be exploited to identify especially powerful rules, in particular those that are common to the different algorithms. This research focuses on the independent application of association and classification mining algorithms to the same data to discover common or similar rules, which are deemed "persistent-rules". The persistent-rule discovery process is demonstrated and tested against two data sets drawn from the American National Election Studies: one data set used to predict voter turnout and the second used to predict vote choice. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：6019 / 6024

页数：6

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