Single-pass algorithms for mining frequency change patterns with limited space in evolving append-only and dynamic transaction data streams

Abstract

We propose an online single-pass algorithm MFC-append (mining frequency change patterns in append-only data streams) for online mining frequent frequency change items in continuous append-only data streams. An online space-efficient data structure called Change-Sketch is developed for providing fast response time to compute dynamic frequency changes between data streams. A modified approach MFC-dynamic (mining frequency change patterns in dynamic data streams) is also presented to mine frequency changes in dynamic data streams. The theoretic analyses show that our algorithms meet the major performance requirements of single-pass, bounded storage, and real time for streaming data mining.