New approaches for mining high utility itemsets with multiple utility thresholds
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10253777" target="_blank" >RIV/61989100:27240/24:10253777 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001129258400002" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001129258400002</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10489-023-05145-8" target="_blank" >10.1007/s10489-023-05145-8</a>
Alternative languages
Result language
angličtina
Original language name
New approaches for mining high utility itemsets with multiple utility thresholds
Original language description
Recently, two research directions have been noticed in data mining: frequent itemset mining (FIM) and high utility itemset mining (HUIM). The FIM process will output itemsets whose number of occurrences together exceeds or equals the required threshold, but this process ignores the beneficial attribute of each item. HUIM algorithms are proposed to overcome the disadvantage of FIM, but these algorithms only use a single threshold, which is unsuitable in the real world when applications often require different utility thresholds. HUIM algorithms with multi-threshold utilities are proposed, but these have high mining time and memory consumption. This paper thus presents an efficient method for Mining High Utility Itemsets with Multiple Utility Thresholds (MHUI-MUT). The article applies upper bounds and the strategy of pruning, thus reducing database scanning, and proposes a cut-off threshold to minimize the mining time.We also present a method to parallelize the algorithm to make the most of the performance of multi-core computers. The experimental results show the superior speed of the MHUI-MUT algorithm compared to the previous one, and the parallel version also outperforms the proposed sequential algorithm. (C) 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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
2024
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
54
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
23
Pages from-to
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UT code for WoS article
001129258400002
EID of the result in the Scopus database
2-s2.0-85180177547