Mining colossal patterns with length constraints
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10249059" target="_blank" >RIV/61989100:27240/21:10249059 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s10489-021-02357-8" target="_blank" >https://link.springer.com/article/10.1007/s10489-021-02357-8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10489-021-02357-8" target="_blank" >10.1007/s10489-021-02357-8</a>
Alternative languages
Result language
angličtina
Original language name
Mining colossal patterns with length constraints
Original language description
Mining of colossal patterns is used to mine patterns in databases with many attributes and values, but the number of instances in each database is small. Although many efficient approaches for extracting colossal patterns have been proposed, they cannot be applied to colossal pattern mining with constraints. In this paper, we solve the challenge of extracting colossal patterns with length constraints. Firstly, we describe the problems of min-length constraint and max-length constraint and combine them with length constraints. After that, we evolve a proposal for efficiently truncating candidates in the mining process and another one for fast checking of candidates. Based on these properties, we offer the mining algorithm of Length Constraints for Colossal Pattern (LCCP) to extract colossal patterns with length constraints. Experiments are also conducted to show the effectiveness of the proposed LCCP algorithm with a comparison to some other ones.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Intelligence
ISSN
0924-669X
e-ISSN
1573-7497
Volume of the periodical
51
Issue of the periodical within the volume
12
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
nestrankovano
UT code for WoS article
000638056400001
EID of the result in the Scopus database
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