A sliding window method for finding recently frequent itemsets over online data streams

Joong Hyuk Chang, Won Suk Lee

Research output: Contribution to journalArticle

110 Citations (Scopus)

Abstract

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is likely to be changed as time goes by. 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 method of finding recently frequent itemsets over an online data stream. The size of a window defines a desired life-time of the information of a transaction in a data stream.

Original languageEnglish
Pages (from-to)753-762
Number of pages10
JournalJournal of Information Science and Engineering
Volume20
Issue number4
Publication statusPublished - 2004 Jul 1

Fingerprint

Approximation algorithms
transaction

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Hardware and Architecture
  • Library and Information Sciences
  • Computational Theory and Mathematics

Cite this

@article{887f693311b64ae79469415069e25ad0,
title = "A sliding window method for finding recently frequent itemsets over online data streams",
abstract = "A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is likely to be changed as time goes by. 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 method of finding recently frequent itemsets over an online data stream. The size of a window defines a desired life-time of the information of a transaction in a data stream.",
author = "Chang, {Joong Hyuk} and Lee, {Won Suk}",
year = "2004",
month = "7",
day = "1",
language = "English",
volume = "20",
pages = "753--762",
journal = "Journal of Information Science and Engineering",
issn = "1016-2364",
publisher = "Institute of Information Science",
number = "4",

}

A sliding window method for finding recently frequent itemsets over online data streams. / Chang, Joong Hyuk; Lee, Won Suk.

In: Journal of Information Science and Engineering, Vol. 20, No. 4, 01.07.2004, p. 753-762.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A sliding window method for finding recently frequent itemsets over online data streams

AU - Chang, Joong Hyuk

AU - Lee, Won Suk

PY - 2004/7/1

Y1 - 2004/7/1

N2 - A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is likely to be changed as time goes by. 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 method of finding recently frequent itemsets over an online data stream. The size of a window defines a desired life-time of the information of a transaction in a data stream.

AB - A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is likely to be changed as time goes by. 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 method of finding recently frequent itemsets over an online data stream. The size of a window defines a desired life-time of the information of a transaction in a data stream.

UR - http://www.scopus.com/inward/record.url?scp=3142639461&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=3142639461&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:3142639461

VL - 20

SP - 753

EP - 762

JO - Journal of Information Science and Engineering

JF - Journal of Information Science and Engineering

SN - 1016-2364

IS - 4

ER -