A complete set of frequent itemsets can get undesirably large due to redundancy when the minimum support threshold is low or when the database is dense. Several concise representat...
Frequent itemset mining is a classic problem in data mining. It is a non-supervised process which concerns in finding frequent patterns (or itemsets) hidden in large volumes of d...
Adriano Veloso, Wagner Meira Jr., Srinivasan Parth...
Recently there is much need of discovering useful knowledge from massive log-data which are generated by Webbased information systems. Such log-data have multiple attributes about...
As new transactions update data sources and subsequently the data warehouse, the previously discovered association rules in the old database may no longer be interesting rules in ...
In this paper, we propose a parallel algorithm for mining maximal frequent itemsets from databases. A frequent itemset is maximal if none of its supersets is frequent. The new par...