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Finding Persistent Strong Rules: Using Classification to Improve Association Mining

Finding Persistent Strong Rules: Using Classification to Improve Association Mining

Anthony Scime, Karthik Rajasethupathy, Kulathur S. Rajasethupathy, Gregg R. Murray
Copyright: © 2013 |Pages: 22
ISBN13: 9781466624559|ISBN10: 1466624558|EISBN13: 9781466624566
DOI: 10.4018/978-1-4666-2455-9.ch002
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MLA

Scime, Anthony, et al. "Finding Persistent Strong Rules: Using Classification to Improve Association Mining." Data Mining: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2013, pp. 28-49. https://doi.org/10.4018/978-1-4666-2455-9.ch002

APA

Scime, A., Rajasethupathy, K., Rajasethupathy, K. S., & Murray, G. R. (2013). Finding Persistent Strong Rules: Using Classification to Improve Association Mining. In I. Management Association (Ed.), Data Mining: Concepts, Methodologies, Tools, and Applications (pp. 28-49). IGI Global. https://doi.org/10.4018/978-1-4666-2455-9.ch002

Chicago

Scime, Anthony, et al. "Finding Persistent Strong Rules: Using Classification to Improve Association Mining." In Data Mining: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 28-49. Hershey, PA: IGI Global, 2013. https://doi.org/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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