The Use of Conventional Clustering Methods Combined with SOM to Increase the Efficiency
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
The Use of Conventional Clustering Methods Combined with SOM to Increase the Efficiency
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/TL02000313" target="_blank" >TL02000313: Intelligent neuro-rehabilitation system for patients with acquired brain damage in early stages of treatment</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
NEURAL COMPUT APPL
ISSN
0941-0643
e-ISSN
1433-3058
Volume of the periodical
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Issue of the periodical within the volume
JUN 2021
Country of publishing house
GB - UNITED KINGDOM
Number of pages
13
Pages from-to
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UT code for WoS article
000665754900001
EID of the result in the Scopus database
2-s2.0-85108601497