Finding "persistent rules": Combining association and classification results

Authors:
Karthik Rajasethupathy;Anthony Scime;Kulathur S. Rajasethupathy;Gregg R. Murray
Affiliations:
Department of Mathematics, 310 Malott Hall, Cornell University, Ithaca, NY 14853-4201, United States;Department of Computer Science, The College at Brockport, State University of New York, 350 New Campus Dr., Brockport, NY 14420-2933, United States;Department of Computer Science, The College at Brockport, State University of New York, 350 New Campus Dr., Brockport, NY 14420-2933, United States;Department of Political Science, Texas Tech University, Box 41015, Lubbock, TX 79409, United States
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 6
Cited 3

Small is beautiful: discovering the minimal set of unexpected patterns

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques

Data mining: concepts and techniques
Using conjunction of attribute values for classification

Proceedings of the eleventh international conference on Information and knowledge management
Interestingness of frequent itemsets using Bayesian networks as background knowledge

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data dimensionality reduction with application to improving classification performance and explaining concepts of data sets

International Journal of Business Intelligence and Data Mining
Vote prediction by iterative domain knowledge and attribute elimination

International Journal of Business Intelligence and Data Mining

Testing terrorism theory with data mining

International Journal of Data Analysis Techniques and Strategies
A New Approach to Associative Classification Based on Binary Multi-objective Particle Swarm Optimization

International Journal of Applied Metaheuristic Computing
Mining stable patterns in multiple correlated databases

Decision Support Systems

Quantified Score

Hi-index	12.05

Visualization

Abstract

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.