New approaches for mining high utility itemsets with multiple utility thresholds

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

New approaches for mining high utility itemsets with multiple utility thresholds

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

New approaches for mining high utility itemsets with multiple utility thresholds

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Applied Intelligence

ISSN

0924-669X

e-ISSN

1573-7497

Svazek periodika

54

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

—

Kód UT WoS článku

001129258400002

EID výsledku v databázi Scopus

2-s2.0-85180177547