S-Divergence-Based Internal Clustering Validation Index

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

S-Divergence-Based Internal Clustering Validation Index

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

S-Divergence-Based Internal Clustering Validation Index

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

International Journal of Interactive Multimedia and Artificial Intelligence

ISSN

1989-1660

e-ISSN

1989-1660

Svazek periodika

8

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

ES - Španělské království

Počet stran výsledku

13

Strana od-do

127-139

Kód UT WoS článku

001130481400007

EID výsledku v databázi Scopus

2-s2.0-85178408829