Abstract
In this paper, we propose a new algorithm, called ClaSP for mining frequent closed sequential patterns in temporal transaction data. Our algorithm uses several efficient search space pruning methods together with a vertical database layout. Experiments on both synthetic and real datasets show that ClaSP outperforms currently well known state of the art methods, such as CloSpan.
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Gomariz, A., Campos, M., Marin, R., Goethals, B. (2013). ClaSP: An Efficient Algorithm for Mining Frequent Closed Sequences. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_5
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DOI: https://doi.org/10.1007/978-3-642-37453-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37452-4
Online ISBN: 978-3-642-37453-1
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