PSO and DE based Novel Quantum Inspired Automatic Clustering Techniques

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10238746" target="_blank" >RIV/61989100:27240/17:10238746 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/17:10238746

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/8234522/authors" target="_blank" >http://ieeexplore.ieee.org/document/8234522/authors</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICRCICN.2017.8234522" target="_blank" >10.1109/ICRCICN.2017.8234522</a>

Jazyk výsledku

angličtina

Název v původním jazyce

PSO and DE based Novel Quantum Inspired Automatic Clustering Techniques

Popis výsledku v původním jazyce

Clustering, a well-known technique, is used to divide a data set into number of groups, called clusters. Differential evolution and particle swarm optimization are robust, fast and very effective search techniques. To increase computational capability, two different quantum inspired meta-heuristics for automatic clustering, have been proposed here. An application of quantum inspired techniques has been demonstrated for automatic clustering of image data sets. These techniques are able to find optimal number of clusters &quot;on the run&quot; for an image data sets. As the comparative research, a comparison has been made between the proposed techniques and their conventional counterparts for four images data set. Effectiveness of the proposed techniques has been exhibited against the fitness value, standard deviation and mean of the fitness, standard error and computational time. Finally, two separate statistical superiority test, referred to as t-test and Friedman test have been performed to prove the superiority the of proposed approaches in their favor.

Název v anglickém jazyce

PSO and DE based Novel Quantum Inspired Automatic Clustering Techniques

Popis výsledku anglicky

Clustering, a well-known technique, is used to divide a data set into number of groups, called clusters. Differential evolution and particle swarm optimization are robust, fast and very effective search techniques. To increase computational capability, two different quantum inspired meta-heuristics for automatic clustering, have been proposed here. An application of quantum inspired techniques has been demonstrated for automatic clustering of image data sets. These techniques are able to find optimal number of clusters &quot;on the run&quot; for an image data sets. As the comparative research, a comparison has been made between the proposed techniques and their conventional counterparts for four images data set. Effectiveness of the proposed techniques has been exhibited against the fitness value, standard deviation and mean of the fitness, standard error and computational time. Finally, two separate statistical superiority test, referred to as t-test and Friedman test have been performed to prove the superiority the of proposed approaches in their favor.

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

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

3rd International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) : proceedings : November 3-5, 2017, Calcutta, India

ISBN

978-1-5386-1931-5

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

6

Strana od-do

285-290

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Kalkata

Datum konání akce

3. 11. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000426611300052