Student research abstract: Mining high average utility pattern using bio-inspired algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F22%3A63556560" target="_blank" >RIV/70883521:28140/22:63556560 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/3477314.3506970" target="_blank" >http://dx.doi.org/10.1145/3477314.3506970</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3477314.3506970" target="_blank" >10.1145/3477314.3506970</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Student research abstract: Mining high average utility pattern using bio-inspired algorithm

Popis výsledku v původním jazyce

High average utility pattern (itemset) Mining (HAUIM) is a necessary research problem in the field of knowledge discovery and data mining. Several algorithms have been proposed to mine high average-utility itemsets (HAUIs). Nonetheless, the large search space leads to poor performance because of excessive execution time and memory usage. To handle this limitation, particle swarm optimization (PSO) is applied to mine HAUIs. In this paper, an effective Binary PSO-based algorithm namely HAUIM-BPSO is proposed to explore HAUI efficiently. In general, HAUIM-BPSO first sets the number of discovered potential high average-utility 1-itemsets (1-PHAUIs) as the size of a particle based on average utility upper bound (AUUB) property. The sigmoid function is also used in the updating process of the individual of the proposed HAUIM-BPSO algorithm. Substantial experiments conducted on publicly available datasets show that the proposed algorithm has better results than existing state-of-the-art algorithms in terms of runtime which can significantly reduce the combinational problem, memory usage, and convergence speed. © 2022 Owner/Author.

Název v anglickém jazyce

Student research abstract: Mining high average utility pattern using bio-inspired algorithm

Popis výsledku anglicky

High average utility pattern (itemset) Mining (HAUIM) is a necessary research problem in the field of knowledge discovery and data mining. Several algorithms have been proposed to mine high average-utility itemsets (HAUIs). Nonetheless, the large search space leads to poor performance because of excessive execution time and memory usage. To handle this limitation, particle swarm optimization (PSO) is applied to mine HAUIs. In this paper, an effective Binary PSO-based algorithm namely HAUIM-BPSO is proposed to explore HAUI efficiently. In general, HAUIM-BPSO first sets the number of discovered potential high average-utility 1-itemsets (1-PHAUIs) as the size of a particle based on average utility upper bound (AUUB) property. The sigmoid function is also used in the updating process of the individual of the proposed HAUIM-BPSO algorithm. Substantial experiments conducted on publicly available datasets show that the proposed algorithm has better results than existing state-of-the-art algorithms in terms of runtime which can significantly reduce the combinational problem, memory usage, and convergence speed. © 2022 Owner/Author.

Druh

D - Stať ve sborníku

CEP obor

—

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

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the ACM Symposium on Applied Computing

ISBN

978-1-4503-8713-2

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

445-448

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

on-line

Datum konání akce

25. 4. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—