Taxonomy-Driven Lumping for Sequence Mining [Abstract] Export

@inproceedings{citeulike:5695629, 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.}, author = {Bonchi, Francesco and Castillo, Carlos and Donato, Debora and Gionis, Aristides}, citeulike-article-id = {5695629}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-642-04180-8_14}, citeulike-linkout-1 = {http://www.springerlink.com/content/v44w38pm83w10222}, doi = {10.1007/978-3-642-04180-8_14}, journal = {Machine Learning and Knowledge Discovery in Databases}, keywords = {clustering, sequence}, pages = {29}, posted-at = {2009-09-01 08:26:56}, priority = {0}, title = {Taxonomy-Driven Lumping for Sequence Mining [Abstract]}, url = {http://dx.doi.org/10.1007/978-3-642-04180-8_14}, year = {2009} }

TY - CONF ID - citeulike:5695629 L3 - citeulike-article-id:5695629 N2 - 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. JF - Machine Learning and Knowledge Discovery in Databases EP - 29 TI - Taxonomy-Driven Lumping for Sequence Mining [Abstract] SP - 29 KW - clustering KW - sequence AU - Bonchi, Francesco AU - Castillo, Carlos AU - Donato, Debora AU - Gionis, Aristides PY - 2009/// UR - http://dx.doi.org/10.1007/978-3-642-04180-8_14 ER -