DESQ: frequent sequence mining with subsequence constraints

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DOI:	https://doi.org/10.1109/ICDM.2016.0092
URL:	https://madoc.bib.uni-mannheim.de/42429
Weitere URL:	http://dws.informatik.uni-mannheim.de/fileadmin/le...
URN:	urn:nbn:de:bsz:180-madoc-424296
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2016
Buchtitel:	16th IEEE International Conference on Data Mining : 12-15 December 2016, Barcelona, Catalonia, Spain : proceedings
Seitenbereich:	793-798
Veranstaltungstitel:	16th International Conference on Data Mining (ICDM)
Veranstaltungsort:	Barcelona, Spain
Veranstaltungsdatum:	12-15 Dec. 2016
Herausgeber:	Bonchi, Francesco
Ort der Veröffentlichung:	Piscataway, NJ
Verlag:	IEEE
ISBN:	978-1-5090-5474-9 , 978-1-5090-5473-2 , 978-1-5090-5472-5
ISSN:	2374-8486
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science I: Data Analytics (Gemulla 2014-)
Fachgebiet:	004 Informatik
Abstract:	Frequent sequence mining methods often make use of constraints to control which subsequences should be mined; e.g., length, gap, span, regular-expression, and hierarchy constraints. We show that many subsequence constraints—including and beyond those considered in the literature—can be unified in a single framework. In more detail, we propose a set of simple and intuitive “pattern expressions” to describe subsequence constraints and explore algorithms for efficiently mining frequent subsequences under such general constraints. A unified treatment allows researchers to study jointly many types of subsequence constraints (instead of each one individually) and helps to improve usability of pattern mining systems for practitioners.