Keywords: recently frequent itemsets, sliding window, data stream, mining data stream, change of data stream
Received June 13, 2003; revised November 13, 2003 & January 29, 2004; accepted February 16, 2004.
Communicated by Ming-Syan Chen.
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Joong Hyuk Chang and Won Suk Lee
Department of Computer Science
Yonsei University
Seoul, 120-749, Korea
E-mail: {jhchang, leewo}@amadeus.yonsei.ac.kr
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.
Received June 13, 2003; revised November 13, 2003 & January 29, 2004; accepted February 16, 2004.
Communicated by Ming-Syan Chen.