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Comparison of FCM and FISODATA

by B. Fergani, Mohamed-khireddine Kholladi, M. Bahri
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 8
Year of Publication: 2012
Authors: B. Fergani, Mohamed-khireddine Kholladi, M. Bahri
10.5120/8913-2960

B. Fergani, Mohamed-khireddine Kholladi, M. Bahri . Comparison of FCM and FISODATA. International Journal of Computer Applications. 56, 8 ( October 2012), 35-39. DOI=10.5120/8913-2960

@article{ 10.5120/8913-2960,
author = { B. Fergani, Mohamed-khireddine Kholladi, M. Bahri },
title = { Comparison of FCM and FISODATA },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 8 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 35-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number8/8913-2960/ },
doi = { 10.5120/8913-2960 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:59:16.718930+05:30
%A B. Fergani
%A Mohamed-khireddine Kholladi
%A M. Bahri
%T Comparison of FCM and FISODATA
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 8
%P 35-39
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In fuzzy clustering, the fuzzy c-means (FCM) clustering algorithm is the best known and used method. An interesting extension of FCM is the fuzzy ISODATA (FISODATA) algorithm; it updates cluster number during the algorithm. That's why we can have more or less clusters than the initialization step. It's the power of the fuzzy ISODATA algorithm comparing to FCM. The aim of this paper is to compare FCM and FISODATA results.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy clustering FCM FISODATA

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