Abstract
In many application domains, events are naturally organized in a hierarchy. Whether events describe human activities, system failures, coordinates in a trajectory, or biomedical phenomena, there is often a taxonomy that should be taken into consideration. A taxonomy allow us to represent the information at a more general description level, if we choose carefully the most suitable level of granularity.
Given a taxonomy of events and a dataset of sequences of these events, we study the problem of finding efficient and effective ways to produce a compact representation of the sequences. This can be valuable by itself, or can be used to help solving other problems, such as clustering.
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This is an extended abstract of an article published in the Data Mining and Knowledge Discovery Journal [1].
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Bonchi, F., Castillo, C., Donato, D., Gionia, A.: Taxonomy-driven lumping for sequence mining. Data Mining and Knowledge Discovery (2009) doi: 10.1007/s10618-009-0141-6
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© 2009 Springer-Verlag Berlin Heidelberg
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Bonchi, F., Castillo, C., Donato, D., Gionis, A. (2009). Taxonomy-Driven Lumping for Sequence Mining. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2009. Lecture Notes in Computer Science(), vol 5781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04180-8_14
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DOI: https://doi.org/10.1007/978-3-642-04180-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04179-2
Online ISBN: 978-3-642-04180-8
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