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 language | English |
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| Pages | 536-539 |
| Number of pages | 4 |
| Publication status | Published - 2003 |
| Event | CIKM 2003: Proceedings of the Twelfth ACM International Conference on Information and Knowledge Management - New Orleans, LA, United States Duration: 2003 Nov 3 → 2003 Nov 8 |
Other
| Other | CIKM 2003: Proceedings of the Twelfth ACM International Conference on Information and Knowledge Management |
|---|---|
| Country/Territory | United States |
| City | New Orleans, LA |
| Period | 03/11/3 → 03/11/8 |
All Science Journal Classification (ASJC) codes
- Decision Sciences(all)
- Business, Management and Accounting(all)