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However, most mining algorithms over a data stream are not able to extract the recent change of knowledge in a data stream adaptively. This is because the obsolete information of old data elements which may be no longer useful or possibly invalid at present is regarded as being as important as that of recent data elements. This paper proposes a sliding window method that finds recently frequent itemsets over a transactional online data stream adaptively. The size of a sliding window defines the desired life-time of information in a newly generated transaction. Consequently, only recently generated transactions in the range of the window are considered to find the recently frequent itemsets of a data stream.<\/jats:p>","DOI":"10.1177\/0165551505050785","type":"journal-article","created":{"date-parts":[[2005,2,21]],"date-time":"2005-02-21T16:24:18Z","timestamp":1109003058000},"page":"76-90","source":"Crossref","is-referenced-by-count":44,"title":["estWin: Online data stream mining of recent frequent itemsets by sliding                 window method"],"prefix":"10.1177","volume":"31","author":[{"given":"Joong Hyuk","family":"Chang","sequence":"first","affiliation":[{"name":"Yonsei University, 134 Shinchon-dong Seodaemun-gu Seoul, 120-749, Korea,"}]},{"given":"Won Suk","family":"Lee","sequence":"additional","affiliation":[{"name":"Yonsei University, 134 Shinchon-dong Seodaemun-gu Seoul, 120-749, Korea"}]}],"member":"179","published-online":{"date-parts":[[2005,4,1]]},"reference":[{"key":"atypb1","first-page":"346","volume-title":"Proceedings of the 28th International Conference on Very Large Databases, Hong Kong, China, August 2002","author":"G. 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