Uncertain frequent itemsets mining algorithm on data streams with constraints

Abstract

Nowadays, many emerging applications in real-life can produce amount of uncertain data streams, while people are often interested in some aspects. To mine constrained frequent itemsets on uncertain data streams, this paper presents a method. First, determining the order of items in the transactions of data streams according to the properties of constraints; then, inserting items into the tree in order; finally, mining constrained frequent itemsets from the tree. Existing algorithms are compared with the proposed method and the performances are analyzed. Results indicate that the proposed method is effective and efficient, which mines constrained frequent itemsets when users request for the mining results and need no additional memory.