Approximate mining of frequent patterns on streams

11 years 9 months ago
Approximate mining of frequent patterns on streams
Abstract. 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.
Claudio Silvestri, Salvatore Orlando
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2007
Where IDA
Authors Claudio Silvestri, Salvatore Orlando
Comments (0)