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Finding Persistent Strong Rules: Using Classification to Improve Association Mining
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Author(s): Anthony Scime (The College at Brockport, State University of New York, USA), Karthik Rajasethupathy (Cornell University, USA), Kulathur S. Rajasethupathy (The College at Brockport, State University of New York, USA)and Gregg R. Murray (Texas Tech University, USA)
Copyright: 2013
Pages: 22
Source title:
Data Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-2455-9.ch002
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Abstract
Data mining is a collection of algorithms for finding interesting and unknown patterns or rules in data. However, different algorithms can result in different rules from the same data. The process presented here exploits these differences to find particularly robust, consistent, and noteworthy rules among much larger potential rule sets. More specifically, this research focuses on using association rules and classification mining to select the persistently strong association rules. Persistently strong association rules are association rules that are verifiable by classification mining the same data set. The process for finding persistent strong rules was executed against two data sets obtained from the American National Election Studies. Analysis of the first data set resulted in one persistent strong rule and one persistent rule, while analysis of the second data set resulted in 11 persistent strong rules and 10 persistent rules. The persistent strong rule discovery process suggests these rules are the most robust, consistent, and noteworthy among the much larger potential rule sets.
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