VGEN: Fast Vertical Mining of Sequential Generator Patterns

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

VGEN: Fast Vertical Mining of Sequential Generator Patterns

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

VGEN: Fast Vertical Mining of Sequential Generator Patterns

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Data Warehousing and Knowledge Discovery

ISBN

978-3-319-10159-0

ISSN

—

e-ISSN

—

Počet stran výsledku

12

Strana od-do

476-488

Název nakladatele

Springer Verlag

Místo vydání

Munich

Místo konání akce

Mnichov

Datum konání akce

2. 9. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—