Simulated Annealing Based Quantum Inspired Automatic Clustering Technique

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241898" target="_blank" >RIV/61989100:27240/18:10241898 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-74690-6_8" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-74690-6_8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-74690-6_8" target="_blank" >10.1007/978-3-319-74690-6_8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Simulated Annealing Based Quantum Inspired Automatic Clustering Technique

Popis výsledku v původním jazyce

Cluster analysis is a popular technique whose aim is to segregate a set of data points into groups, called clusters. Simulated Annealing (SA) is a popular meta-heuristic inspired by the annealing process used in metallurgy, useful in solving complex optimization problems. In this paper, the use of the Quantum Computing (QC) and SA is explored to design Quantum Inspired Simulated Annealing technique, which can be applied to compute optimum number of clusters for image clustering. Experimental results over a number of images endorse the effectiveness of the proposed technique pertaining to fitness value, convergence time, accuracy, robustness, and standard error. The paper also reports the computation results of a statistical superiority test, known as t-test. An experimental judgement to the classical technique has also be presented, which eventually demonstrates that the proposed technique outperforms the other. (C) 2018, Springer International Publishing AG.

Název v anglickém jazyce

Simulated Annealing Based Quantum Inspired Automatic Clustering Technique

Popis výsledku anglicky

Cluster analysis is a popular technique whose aim is to segregate a set of data points into groups, called clusters. Simulated Annealing (SA) is a popular meta-heuristic inspired by the annealing process used in metallurgy, useful in solving complex optimization problems. In this paper, the use of the Quantum Computing (QC) and SA is explored to design Quantum Inspired Simulated Annealing technique, which can be applied to compute optimum number of clusters for image clustering. Experimental results over a number of images endorse the effectiveness of the proposed technique pertaining to fitness value, convergence time, accuracy, robustness, and standard error. The paper also reports the computation results of a statistical superiority test, known as t-test. An experimental judgement to the classical technique has also be presented, which eventually demonstrates that the proposed technique outperforms the other. (C) 2018, Springer International Publishing AG.

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

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 723

ISBN

978-3-319-74689-0

ISSN

2194-5357

e-ISSN

2194-5365

Počet stran výsledku

9

Strana od-do

73-81

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Káhira

Datum konání akce

22. 2. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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