Association rule mining is a widely used data mining technique, but can provide an overwhelming number of discovered rules. The objective of this project was to investigate the post-processing of association rules to automatically select the most relevant rules and facilitate feasible human analysis. The post-processing techniques used included pruning uninteresting rules, pruning redundant rules and summarizing rules with Direction Setting Rules. These techniques were implemented in the WPI-WEKA data mining system and then tested for efficiency and effectiveness.
Worcester Polytechnic Institute
Major Qualifying Project
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