A hierarchical set-enumeration tree enabling high occupancy item set mining and the use of an adaptive occupancy threshold

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10256169" target="_blank" >RIV/61989100:27240/25:10256169 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/25:10256169

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s10489-024-06166-7" target="_blank" >https://link.springer.com/article/10.1007/s10489-024-06166-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10489-024-06166-7" target="_blank" >10.1007/s10489-024-06166-7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A hierarchical set-enumeration tree enabling high occupancy item set mining and the use of an adaptive occupancy threshold

Popis výsledku v původním jazyce

The highly efficient HEP algorithm is a useful tool for mining High Occupancy (HO) item sets. Occupancy is an important measure that describes the interestingness of frequent item sets. The current study examines the efficiency problems in mining HO item sets and proposes an improved HEP algorithm, named advanced HEP (A-HEP), based on set theory rules which eliminate a large number of redundant iterations. The study also proposes a novel adaptive-and-modified HEP (NAM-HEP) algorithm that uses HO Set-Enumeration (SE) trees to store HO item sets. The study proposes definitions for adaptive thresholds such as support threshold and occupancy threshold based on the attributes of the transaction database for efficient pruning of the HO-SE tree. Two pseudo-code blocks are presented in addition to a detailed description of the A-HEP and NAM-HEP algorithms and their advantages. Using the A-HEP and NAM-HEP algorithms, HO item sets are investigated from the practical transaction databases named mushroom and retail. The results indicate that the proposed A-HEP and NAM-HEP algorithms enhance mining performance and runtime benchmarks.

Název v anglickém jazyce

A hierarchical set-enumeration tree enabling high occupancy item set mining and the use of an adaptive occupancy threshold

Popis výsledku anglicky

The highly efficient HEP algorithm is a useful tool for mining High Occupancy (HO) item sets. Occupancy is an important measure that describes the interestingness of frequent item sets. The current study examines the efficiency problems in mining HO item sets and proposes an improved HEP algorithm, named advanced HEP (A-HEP), based on set theory rules which eliminate a large number of redundant iterations. The study also proposes a novel adaptive-and-modified HEP (NAM-HEP) algorithm that uses HO Set-Enumeration (SE) trees to store HO item sets. The study proposes definitions for adaptive thresholds such as support threshold and occupancy threshold based on the attributes of the transaction database for efficient pruning of the HO-SE tree. Two pseudo-code blocks are presented in addition to a detailed description of the A-HEP and NAM-HEP algorithms and their advantages. Using the A-HEP and NAM-HEP algorithms, HO item sets are investigated from the practical transaction databases named mushroom and retail. The results indicate that the proposed A-HEP and NAM-HEP algorithms enhance mining performance and runtime benchmarks.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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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

2025

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

55

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

"55:205"

Kód UT WoS článku

001383333200008

EID výsledku v databázi Scopus

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