A comparative analysis of frequent pattern mining algorithms used for streaming data

Abstract

Frequent pattern mining across streaming data i s a challenging task. It require real time response and incurs great computational complexity. In this paper, we discuss challenges of developing frequent pattern mining algorithms for streaming data, compare three algorithms proposed in literature and explore scope of improvement in the algorithms. We discuss the suitability of these algorithms according to applications.