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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • 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

  • 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