Conference paper

FOURNIER-VIGER Philippe, GOMARIZ Antonio, ŠEBEK Michal and HLOSTA Martin. VGEN: Fast Vertical Mining of Sequential Generator Patterns. In: Data Warehousing and Knowledge Discovery. Munich: Springer Verlag, 2014, pp. 476-488. ISBN 978-3-319-10159-0. Available from:
Publication language:english
Original title:VGEN: Fast Vertical Mining of Sequential Generator Patterns
Title (cs):VGEN: Rychlé vertikální dolování sekvenčních generátorů
Proceedings:Data Warehousing and Knowledge Discovery
Conference:16th International Conference on Data Warehousing and Knowledge Discovery
Place:Munich, DE
Publisher:Springer Verlag
sequential patterns, generators, vertical mining, candidate pruning
Sequential pattern mining is a popular data mining task with wide applications. However, the set of all sequential patterns can be very large. To discover fewer but more representative patterns, several compact representations of sequential patterns have been studied. The set of sequential generatorsis one the most popular representations. It was shown to provide higher accuracy for classification than using all or only closed sequential patterns. Furthermore, mining generators is a key step in several other data mining tasks such as sequential rule generation. However, mining generators is computationally expensive. To address this issue, we propose a novel mining algorithm namedVGEN (Vertical sequential GENerator miner). An experimental study on five real datasets shows that VGEN is up to two orders of magnitude faster than the state-of-the-art algorithms for sequential generator mining.
   author = {Philippe Fournier-Viger and Antonio Gomariz and
	Michal {\v{S}}ebek and Martin Hlosta},
   title = {VGEN: Fast Vertical Mining of Sequential Generator
   pages = {476--488},
   booktitle = {Data Warehousing and Knowledge Discovery},
   year = {2014},
   location = {Munich, DE},
   publisher = {Springer Verlag},
   ISBN = {978-3-319-10159-0},
   doi = {10.1007/978-3-319-10160-6_42},
   language = {english},
   url = {}

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