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 language | English |
|---|---|
| Pages (from-to) | 753-762 |
| Number of pages | 10 |
| Journal | Journal of Information Science and Engineering |
| Volume | 20 |
| Issue number | 4 |
| Publication status | Published - 2004 Jul 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Human-Computer Interaction
- Hardware and Architecture
- Library and Information Sciences
- Computational Theory and Mathematics
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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 journal › Article
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 -