Approximate mining of frequent patterns on streams

This paper introduces a new algorithm for approximate mining of frequent patterns from streams of transactions using a limited amount of memory. The proposed algorithm consists in the computation of frequent itemsets in recent data and an effective method for inferring the global support of previously infrequent itemsets. Both upper and lower bounds on the support of each pattern found are returned along with the interpolated support. An extensive experimental evaluation shows that APStream, the proposed algorithm, yields a good approximation of the exact global result considering both the set of patterns found and their support.