Fuzzy K-Means Using Non-Linear S-Distance
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F19%3A50015727" target="_blank" >RIV/62690094:18450/19:50015727 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8693780" target="_blank" >https://ieeexplore.ieee.org/document/8693780</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2019.2910195" target="_blank" >10.1109/ACCESS.2019.2910195</a>
Alternative languages
Result language
angličtina
Original language name
Fuzzy K-Means Using Non-Linear S-Distance
Original language description
A considerable amount of research has been done since long to select an appropriate similarity or dissimilarity measure in cluster analysis for exposing the natural grouping in an input dataset. Still, it is an open problem. In recent years, the research community is focusing on divergence-based non-Euclidean similarity measure in partitional clustering for grouping. In this paper, the Euclidean distance of traditional Fuzzy k-means (FKM) algorithm is replaced by the S-distance, which is derived from the newly introduced S-divergence. Few imperative properties of S-distance and modified FKM are presented in this study. The performance of the proposed FKM is compared with the conventional FKM with Euclidean distance and its variants with the help of several synthetic and real-world datasets. This study focuses on how the proposed clustering algorithm performs on the adopted datasets empirically. The comparative study illustrates that the obtained results are convincing. Moreover, the achieved results denote that the modified FKM outperforms some state-of-the-art FKM algorithms.
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
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE ACCESS
ISSN
2169-3536
e-ISSN
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Volume of the periodical
7
Issue of the periodical within the volume
April
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
55121-55131
UT code for WoS article
000467985000001
EID of the result in the Scopus database
2-s2.0-85067021770