VGEN: Fast Vertical Mining of Sequential Generator Patterns
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F14%3APU111959" target="_blank" >RIV/00216305:26230/14:PU111959 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-10160-6_42" target="_blank" >http://dx.doi.org/10.1007/978-3-319-10160-6_42</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-10160-6_42" target="_blank" >10.1007/978-3-319-10160-6_42</a>
Alternative languages
Result language
angličtina
Original language name
VGEN: Fast Vertical Mining of Sequential Generator Patterns
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Data Warehousing and Knowledge Discovery
ISBN
978-3-319-10159-0
ISSN
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e-ISSN
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Number of pages
12
Pages from-to
476-488
Publisher name
Springer Verlag
Place of publication
Munich
Event location
Mnichov
Event date
Sep 2, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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