estWin: Adaptively monitoring the recent change of frequent itemsets over online data streams

Joong Hyuk Chang, Won Suk Lee

Research output: Contribution to conferencePaper

30 Citations (Scopus)

Abstract

Knowledge embedded in a data stream is likely to be changed as time goes by. Consequently, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. However, most of mining algorithms or frequency approximation 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-based method that finds recently frequent itemsets over an online data stream adaptively. The size of a window defines a desired life-time of the information in a newly generated transaction. Consequently, only recently generated transactions in the range of the window are considered to find the frequent itemsets of a data stream.

Original languageEnglish
Pages536-539
Number of pages4
Publication statusPublished - 2003
EventCIKM 2003: Proceedings of the Twelfth ACM International Conference on Information and Knowledge Management - New Orleans, LA, United States
Duration: 2003 Nov 32003 Nov 8

Other

OtherCIKM 2003: Proceedings of the Twelfth ACM International Conference on Information and Knowledge Management
CountryUnited States
CityNew Orleans, LA
Period03/11/303/11/8

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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