Sequential pattern mining using IDLists

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F20%3A63526960" target="_blank" >RIV/70883521:28140/20:63526960 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-63007-2_27" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-63007-2_27</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-63007-2_27" target="_blank" >10.1007/978-3-030-63007-2_27</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sequential pattern mining using IDLists

Popis výsledku v původním jazyce

Sequential pattern mining is a practical problem whose objective is to discover helpful informative patterns in a stored database such as market transaction databases. It covers many applications in different areas. Recently, a study that improved the runtime for mining patterns was proposed. It was called pseudo-IDLists and it helps prevent duplicate data from replicating during the mining process. However, the idea only works for the special type of sequential patterns, which are clickstream patterns. Direct applying the idea for sequential pattern mining is not feasible. Hence, we proposed adaptions and changes to the novel idea and proposed SUI (Sequential pattern mining Using IDList), a sequential pattern mining algorithm based on pseudo-IDLists. Via experiments on three test databases, we show that SUI is efficient and effective regarding runtime and memory consumption. © 2020, Springer Nature Switzerland AG.

Název v anglickém jazyce

Sequential pattern mining using IDLists

Popis výsledku anglicky

Sequential pattern mining is a practical problem whose objective is to discover helpful informative patterns in a stored database such as market transaction databases. It covers many applications in different areas. Recently, a study that improved the runtime for mining patterns was proposed. It was called pseudo-IDLists and it helps prevent duplicate data from replicating during the mining process. However, the idea only works for the special type of sequential patterns, which are clickstream patterns. Direct applying the idea for sequential pattern mining is not feasible. Hence, we proposed adaptions and changes to the novel idea and proposed SUI (Sequential pattern mining Using IDList), a sequential pattern mining algorithm based on pseudo-IDLists. Via experiments on three test databases, we show that SUI is efficient and effective regarding runtime and memory consumption. © 2020, Springer Nature Switzerland AG.

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

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-642-29352-8

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

13

Strana od-do

341-353

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Heidelberg

Místo konání akce

Da Nang

Datum konání akce

30. 11. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—