A Sliding Window Method for discovery of Recently Frequent Item sets over Online Data Streams

Abstract

A data stream is a massive unrestrained sequence of data elements continuously generated at a rapid rate.
Consequently, the knowledge entrenched in a data stream is likely to be changed as time goes by. However, most of
mining algorithms or frequency estimate algorithms for a data stream do not able to extract the recent change of
information in a data stream adaptively. This paper proposes a sliding window method of discovery of recently
frequent itemsets over an online data stream. The size of a window defines a favored life time of the information of a
transaction in a data stream.