Sequential pattern mining using IDLists
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Sequential pattern mining using IDLists
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
—
Number of pages
13
Pages from-to
341-353
Publisher name
Springer Science and Business Media Deutschland GmbH
Place of publication
Heidelberg
Event location
Da Nang
Event date
Nov 30, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—