A fast parallel algorithm for frequent itemsets mining

Abstract

Mining frequent itemsets from leirge databases is an important computational task with a lot of applications. The most known among them is the market-basket problem which assumes that we have a large number of items and we want to know which items are bought together. A recent application is that of web pages (baskets) and linked pages (items). Pages with many common references may be about the same topic. In this paper we present a parallel algorithm for mining frequent itemsets. We provide experimental evidence that our algorithm scales quite well and we discuss the merits of parallelization for this problem.