A quantum inspired differential evolution algorithm for automatic clustering of real life datasets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10254650" target="_blank" >RIV/61989100:27240/24:10254650 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s11042-023-15704-3" target="_blank" >https://link.springer.com/article/10.1007/s11042-023-15704-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11042-023-15704-3" target="_blank" >10.1007/s11042-023-15704-3</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A quantum inspired differential evolution algorithm for automatic clustering of real life datasets

Popis výsledku v původním jazyce

In recent years, Quantum Inspired Metaheuristic algorithms have emerged to be promising due to their efficiency, robustness and faster computational capability. In this paper, a novel Quantum Inspired Differential Evolution (QIDE) algorithm has been presented for automatic clustering of unlabeled datasets. In case of automatic clustering, the datasets have been clustered into optimal number of groups on the run without any apriori knowledge of the datasets. In this work, the proposed algorithm has been compared with other two quantum inspired algorithms, viz., Fast Quantum Inspired Evolutionary Clustering Algorithm (FQEA) and Quantum Evolutionary Algorithm for Data Clustering (QEAC), a Classical Differential Evolution (CDE) algorithm with different mutation probabilities and an Improved Differential Evolution (IDE) algorithm. The experiments have been conducted on six real life publicly available datasets to identify the optimal number of clusters. By introducing some concepts of quantum gates, the proposed algorithm not only achieves good convergence speed but also provides better results than other competitive algorithms. In addition, Sobol&apos;s sensitivity analysis has been conducted for tuning the parameters of the proposed algorithm.

Název v anglickém jazyce

A quantum inspired differential evolution algorithm for automatic clustering of real life datasets

Popis výsledku anglicky

In recent years, Quantum Inspired Metaheuristic algorithms have emerged to be promising due to their efficiency, robustness and faster computational capability. In this paper, a novel Quantum Inspired Differential Evolution (QIDE) algorithm has been presented for automatic clustering of unlabeled datasets. In case of automatic clustering, the datasets have been clustered into optimal number of groups on the run without any apriori knowledge of the datasets. In this work, the proposed algorithm has been compared with other two quantum inspired algorithms, viz., Fast Quantum Inspired Evolutionary Clustering Algorithm (FQEA) and Quantum Evolutionary Algorithm for Data Clustering (QEAC), a Classical Differential Evolution (CDE) algorithm with different mutation probabilities and an Improved Differential Evolution (IDE) algorithm. The experiments have been conducted on six real life publicly available datasets to identify the optimal number of clusters. By introducing some concepts of quantum gates, the proposed algorithm not only achieves good convergence speed but also provides better results than other competitive algorithms. In addition, Sobol&apos;s sensitivity analysis has been conducted for tuning the parameters of the proposed algorithm.

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

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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

Multimedia Tools and Applications

ISSN

1380-7501

e-ISSN

1573-7721

Svazek periodika

83

Číslo periodika v rámci svazku

2024

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

30

Strana od-do

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Kód UT WoS článku

001010496600003

EID výsledku v databázi Scopus

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