Mining frequent items in a stream using flexible windows

Issue title: Knowledge Discovery from Data Streams

Guest editors: João Gamax, Jesus Aguilar-Ruizy and Ralf Klinkenbergz

Article type: Research Article

Authors: Calders, Toon^a | Dexters, Nele^b | Goethals, Bart^b

Affiliations: [a] TU Eindhoven, Eindhoven, The Netherlands | [b] University of Antwerp, Antwerp, Belgium | [x] LIAAD-University of Porto, Porto, Portugal | [y] Polytechnic Pablo de Olavide University, Seville, Spain | [z] University of Dortmund, Dortmund, Germany

Abstract: We study the problem of finding frequent items in a continuous stream of itemsets. A new frequency measure is introduced, based on a flexible window length. For a given item, its current frequency in the stream is defined as the maximal frequency over all windows from any point in the past until the current state. We study the properties of the new measure, and propose an incremental algorithm that allows to produce the current frequency of an item immediately at any time. It is shown experimentally that the memory requirements of the algorithm are extremely small for many different realistic data distributions.

DOI: 10.3233/IDA-2008-12304

Journal: Intelligent Data Analysis, vol. 12, no. 3, pp. 293-304, 2008