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 |
All Science Journal Classification (ASJC) codes
- Software
- Human-Computer Interaction
- Hardware and Architecture
- Library and Information Sciences
- Computational Theory and Mathematics