Concurrent Processing of Frequent Itemset Queries Using FP-Growth Algorithm

Discovery of frequent itemsets is a very important data mining problem with numerous applications. Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. A significant amount of research on frequent itemset mining has been done so far, focusing mainly on developing faster complete mining algorithms, efficient constraint handling, and reusing results of previous queries. Recently, a new problem of optimizing processing of batches of frequent itemset queries has been considered and two multiple query optimization techniques for frequent itemset queries: Common Counting and Mine Merge have been proposed. Mine Merge does not depend on a particular mining algorithm, while Common Counting has been specifically designed to work with Apriori. Nevertheless, in previous works the efficiency of Mine Merge was tested only on Apriori, and it is unclear how it would perform with newer pattern-growth algorithms like FP-growth. In this paper we adapt the Common Counting method to work with FP-growth and evaluate efficiency of both methods when FP-growth is used as a basic mining algorithm.