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In this paper, a mining method of sequential patterns over an online sequence data stream is proposed, which is useful for retrieving embedded knowledge in the data stream. The proposed method can minimize memory usage of the mining process while an error is allowed in its mining result, and supports flexible trade-off between memory usage and mining accuracy. However, the error is minimized by an accurate estimation method for the count of a sequence, which considers the ordering information of items. The proposed method can catch a recent change in a sequence data stream in a short time, by a decaying mechanism gracefully discarding old information that may be no longer useful.<\/jats:p>","DOI":"10.1177\/0165551505055405","type":"journal-article","created":{"date-parts":[[2005,8,16]],"date-time":"2005-08-16T11:08:32Z","timestamp":1124190512000},"page":"420-432","source":"Crossref","is-referenced-by-count":27,"title":["Efficient mining method for retrieving sequential patterns over online data streams"],"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":[[2005,10,1]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1177\/0165551504047928"},{"key":"atypb2","volume-title":"Principles of Data Mining","author":"D.J. 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