S-Divergence-Based Internal Clustering Validation Index
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020881" target="_blank" >RIV/62690094:18450/23:50020881 - isvavai.cz</a>
Result on the web
<a href="https://www.ijimai.org/journal/sites/default/files/2023-11/ijimai8_4_12.pdf" target="_blank" >https://www.ijimai.org/journal/sites/default/files/2023-11/ijimai8_4_12.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.9781/ijimai.2023.10.001" target="_blank" >10.9781/ijimai.2023.10.001</a>
Alternative languages
Result language
angličtina
Original language name
S-Divergence-Based Internal Clustering Validation Index
Original language description
A clustering validation index (CVI) is employed to evaluate an algorithm’s clustering results. Generally, CVI statistics can be split into three classes, namely internal, external, and relative cluster validations. Most of the existing internal CVIs were designed based on compactness (CM) and separation (SM). The distance between cluster centers is calculated by SM, whereas the CM measures the variance of the cluster. However, the SM between groups is not always captured accurately in highly overlapping classes. In this article, we devise a novel internal CVI that can be regarded as a complementary measure to the landscape of available internaCVIs. Initially, a database’s clusters are modeled as a non-parametric density function estimated using kernedensity estimation. Then the S-divergence (SD) and S-distance are introduced for measuring the SM and the CM, respectively. The SD is defined based on the concept of Hermitian positive definite matrices applied to density functions. The proposed internal CVI (PM) is the ratio of CM to SM. The PM outperforms the legacy measures presented in the literature on both superficial and realistic databases in various scenarios, according to empirical results from four popular clustering algorithms, including fuzzy k-means, spectral clusteringdensity peak clustering, and density-based spatial clustering applied to noisy data. © 2023, Universidad Internacional de la Rioja. All rights reserved.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Interactive Multimedia and Artificial Intelligence
ISSN
1989-1660
e-ISSN
1989-1660
Volume of the periodical
8
Issue of the periodical within the volume
4
Country of publishing house
ES - SPAIN
Number of pages
13
Pages from-to
127-139
UT code for WoS article
001130481400007
EID of the result in the Scopus database
2-s2.0-85178408829