Inductive databases (IDBs) have been proposed to afford the problem of knowledge discovery from huge databases. With an IDB the user/analyst performs a set of very different operat...
We present AIM2-F, an improved implementation of AIM-F [4] algorithm for mining frequent itemsets. Past studies have proposed various algorithms and techniques for improving the e...
We present a performance study of the MAFIA algorithm for mining maximal frequent itemsets from a transactional database. In a thorough experimental analysis, we isolate the effec...
Douglas Burdick, Manuel Calimlim, Jason Flannick, ...
In this work we focus on the problem of frequent itemset mining on large, out-of-core data sets. After presenting a characterization of existing out-of-core frequent itemset minin...
Mining of frequent closed itemsets has been shown to be more efficient than mining frequent itemsets for generating non-redundant association rules. The task is challenging in data...