Hybrid Differential Evolution Algorithm for Optimal Clustering

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F15%3AA1600S4B" target="_blank" >RIV/61988987:17610/15:A1600S4B - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Hybrid Differential Evolution Algorithm for Optimal Clustering

Popis výsledku v původním jazyce

The problem of optimal non-hierarchical clustering is addressed. A new algorithm combining differential evolution and $k$-means is proposed and tested on eight well-known real-world data sets. The classification of objects to be optimized is encoded by the cluster centers in differential evolution (DE) algorithm. A~new efficient heuristic for this rearrangement was also proposed. The plain DE variants with and without the rearrangement were compared with corresponding hybrid k-means variants. The experimental results showed that hybrid variants with k-means algorithm are essentially more efficient than the non-hybrid ones. Compared to a standard k-means algorithm with restart, the new hybrid algorithm appeared more reliable and efficient, especially inmore difficult tasks. The results for TRW and VCR criterion were compared. Both criteria provided the same optimal partitions and no significant differences were found in efficiency of the algorithms using these criteria.

Název v anglickém jazyce

Hybrid Differential Evolution Algorithm for Optimal Clustering

Popis výsledku anglicky

The problem of optimal non-hierarchical clustering is addressed. A new algorithm combining differential evolution and $k$-means is proposed and tested on eight well-known real-world data sets. The classification of objects to be optimized is encoded by the cluster centers in differential evolution (DE) algorithm. A~new efficient heuristic for this rearrangement was also proposed. The plain DE variants with and without the rearrangement were compared with corresponding hybrid k-means variants. The experimental results showed that hybrid variants with k-means algorithm are essentially more efficient than the non-hybrid ones. Compared to a standard k-means algorithm with restart, the new hybrid algorithm appeared more reliable and efficient, especially inmore difficult tasks. The results for TRW and VCR criterion were compared. Both criteria provided the same optimal partitions and no significant differences were found in efficiency of the algorithms using these criteria.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/ED1.1.00%2F02.0070" target="_blank" >ED1.1.00/02.0070: Centrum excelence IT4Innovations</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

APPL SOFT COMPUT

ISSN

1568-4946

e-ISSN

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Svazek periodika

35

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

502-512

Kód UT WoS článku

000360109900037

EID výsledku v databázi Scopus

2-s2.0-84937416053