The Use of Conventional Clustering Methods Combined with SOM to Increase the Efficiency

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F21%3AA22026UY" target="_blank" >RIV/61988987:17310/21:A22026UY - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007%2Fs00521-021-06251-9" target="_blank" >https://link.springer.com/article/10.1007%2Fs00521-021-06251-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-021-06251-9" target="_blank" >10.1007/s00521-021-06251-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The Use of Conventional Clustering Methods Combined with SOM to Increase the Efficiency

Popis výsledku v původním jazyce

This article reflects research in the field of artificial intelligence and demonstrates a higher efficiency achievement of conventional clustering methods in combination with unconventional methods. It concerns a new hybrid approach based on the SOM (Self Organizing Maps) method. We focused on the possibility of combining SOM with other clustering methods - CLARA, CURE a K-means. Method SOM is primarily useful in the first phases of the process, where knowledge of the data is too vague. It is thus followed by the use of a selected clustering algorithm. It then works with preprocessed data. Its performance, compared with its outputs on unprocessed data, is more efficient, which is proved by the performed experimental study on the benchmark data set Fundamental Clustering Problems Suite (FCPS). Part of the experimental verification was also a comparison of the achieved outputs with other approaches using this dataset based on a standard metrics - Rand index.

Název v anglickém jazyce

The Use of Conventional Clustering Methods Combined with SOM to Increase the Efficiency

Popis výsledku anglicky

This article reflects research in the field of artificial intelligence and demonstrates a higher efficiency achievement of conventional clustering methods in combination with unconventional methods. It concerns a new hybrid approach based on the SOM (Self Organizing Maps) method. We focused on the possibility of combining SOM with other clustering methods - CLARA, CURE a K-means. Method SOM is primarily useful in the first phases of the process, where knowledge of the data is too vague. It is thus followed by the use of a selected clustering algorithm. It then works with preprocessed data. Its performance, compared with its outputs on unprocessed data, is more efficient, which is proved by the performed experimental study on the benchmark data set Fundamental Clustering Problems Suite (FCPS). Part of the experimental verification was also a comparison of the achieved outputs with other approaches using this dataset based on a standard metrics - Rand index.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/TL02000313" target="_blank" >TL02000313: Chytrý neuro-rehabilitační systém pro pacienty se získaným poškozením mozku v časných stádiích léčby</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

NEURAL COMPUT APPL

ISSN

0941-0643

e-ISSN

1433-3058

Svazek periodika

—

Číslo periodika v rámci svazku

JUN 2021

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

—

Kód UT WoS článku

000665754900001

EID výsledku v databázi Scopus

2-s2.0-85108601497