SFCM: A Fuzzy Clustering Algorithm of Extracting the Shape Information of Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10246971" target="_blank" >RIV/61989100:27240/21:10246971 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/9160881" target="_blank" >https://ieeexplore.ieee.org/document/9160881</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TFUZZ.2020.3014662" target="_blank" >10.1109/TFUZZ.2020.3014662</a>

Result language

angličtina

Original language name

SFCM: A Fuzzy Clustering Algorithm of Extracting the Shape Information of Data

Original language description

Topological data analysis is a new theoretical trend using topological techniques to mine data. This approach helps determine topological data structures. It focuses on investigating the global shape of data rather than on local information of high-dimensional data. The Mapper algorithm is considered as a sound representative approach in this area. It is used to cluster and identify concise and meaningful global topological data structures that are out of reach for many other clustering methods. In this article, we propose a new method called the Shape Fuzzy C-Means (SFCM) algorithm, which is constructed based on the Fuzzy C-Means algorithm with particular features of the Mapper algorithm. The SFCM algorithm can not only exhibit the same clustering ability as the Fuzzy C-Means but also reveal some relationships through visualizing the global shape of data supplied by the Mapper. We present a formal proof and include experiments to confirm our claims. The performance of the enhanced algorithm is demonstrated through a comparative analysis involving the original algorithm, Mapper, and the other fuzzy set based improved algorithm, F-Mapper, for synthetic and real-world data. The comparison is conducted with respect to output visualization in the topological sense and clustering stability. (C) 1993-2012 IEEE.

Czech name

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Czech description

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Type

J_imp - 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)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

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

Name of the periodical

IEEE Transactions on Fuzzy Systems

ISSN

1063-6706

e-ISSN

—

Volume of the periodical

29

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

75-89

UT code for WoS article

000605370700007

EID of the result in the Scopus database

2-s2.0-85098858446