Abstract | ||
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With the usefulness of data mining in various fields of information science, various mining methods have been proposed in previous research. Recently, in these fields, data has taken the form of continuous data streams rather than finite stored data sets. 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. |
Year | DOI | Venue |
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2005 | 10.1177/0165551505055405 | J. Information Science |
Keywords | DocType | Volume |
sequential pattern,mining method,mining accuracy,data mining,continuous data stream,online sequence data stream,sequence data stream,memory usage,efficient mining method,data set,online data stream,data stream,proposed method | Journal | 31 |
Issue | ISSN | Citations |
5 | 0165-5515 | 17 |
PageRank | References | Authors |
0.80 | 21 | 2 |
Name | Order | Citations | PageRank |
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Joong Hyuk Chang | 1 | 401 | 19.81 |
Won Suk Lee | 2 | 536 | 51.26 |