Abstract
Due to the complexity of the task, partially ordered pattern mining of sequential data has not been subject to much study, despite its usefulness. This paper investigates this data mining challenge by describing OrderSpan, a new algorithm that extracts such patterns from sequential databases and overcomes some of the drawbacks of existing methods. Our work consists in providing a simple and flexible framework to directly mine complex sequences of itemsets, by combining well-known properties on prefixes and suffixes. Experiments were performed on different real datasets to show the benefit of partially ordered patterns.
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Fabrègue, M., Braud, A., Bringay, S., Le Ber, F., Teisseire, M. (2013). OrderSpan: Mining Closed Partially Ordered Patterns. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds) Advances in Intelligent Data Analysis XII. IDA 2013. Lecture Notes in Computer Science, vol 8207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41398-8_17
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DOI: https://doi.org/10.1007/978-3-642-41398-8_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41397-1
Online ISBN: 978-3-642-41398-8
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