Efficient mining method for retrieving sequential patterns over online data streams
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.
sequential pattern data stream embedded knowledge significant sequence information retrieval