Integrated Candidate Generation in Processing Batches of Frequent Itemset Queries using Apriori

Frequent itemset mining can be regarded as advanced database querying where a user specifies constraints on the source dataset and patterns to be discovered. Since such frequent itemset queries can be submitted to the data mining system in batches, a natural question arises whether a batch of queries can be processed more efficiently than by executing each query individually. So far, two methods of processing batches of frequent itemset queries have been proposed for the Apriori algorithm: Common Counting, which integrates only the database scans required to process the queries, and Common Candidate Tree, which extends the concept by allowing the queries to also share their main memory structures. In this paper we propose a new method called Common Candidates, which further integrates processing of the queries from a batch by performing integrated candidate generation.