Abstract
We present a new algorithm of mining sequential patterns in data stream. In recent years data stream emerges as a new data type in many applications. When processing data stream, the memory is fixed, new stream elements flow continuously. The stream data can not be paused or completely stored. We develop a LSP-tree data structure to store the discovered sequential patterns. The experiment result shows that our proposal is able to mine sequential patterns from stream data with rather low price.
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Huang, Q., Ouyang, W. (2009). Mining Sequential Patterns in Data Stream. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_98
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DOI: https://doi.org/10.1007/978-3-642-01510-6_98
Publisher Name: Springer, Berlin, Heidelberg
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