MINING NON-REDUNDANT FREQUENT PATTERNS IN MULTI-LEVEL DATASETS USING MIN MAX APPROXIMATE RULES
Rajanala Vijaya Prakash, Aliseri Govardhan · 2012
Frequent Patterns are useful for many data mining tasks, including the popular Association Rule mining task but also Feature Construction, Association-based Classification, Clustering, etc.. But, the discovered Association Rules from the Frequent Itemsets are huge and many of them are redundant, especially for multi-level datasets. This proposal allows the removal of redundant rules in multi-level datasets through the use of Min-Max Approximate rule.