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Unsupervised Data Classification for Convex and Non Convex Classes

by Fairouz Lekhal, Mohamed El Hitmy, Ouafae El Melhaoui
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 24 - Number 1
Year of Publication: 2011
Authors: Fairouz Lekhal, Mohamed El Hitmy, Ouafae El Melhaoui
10.5120/2917-3843

Fairouz Lekhal, Mohamed El Hitmy, Ouafae El Melhaoui . Unsupervised Data Classification for Convex and Non Convex Classes. International Journal of Computer Applications. 24, 1 ( June 2011), 8-15. DOI=10.5120/2917-3843

@article{ 10.5120/2917-3843,
author = { Fairouz Lekhal, Mohamed El Hitmy, Ouafae El Melhaoui },
title = { Unsupervised Data Classification for Convex and Non Convex Classes },
journal = { International Journal of Computer Applications },
issue_date = { June 2011 },
volume = { 24 },
number = { 1 },
month = { June },
year = { 2011 },
issn = { 0975-8887 },
pages = { 8-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume24/number1/2917-3843/ },
doi = { 10.5120/2917-3843 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:09:49.889050+05:30
%A Fairouz Lekhal
%A Mohamed El Hitmy
%A Ouafae El Melhaoui
%T Unsupervised Data Classification for Convex and Non Convex Classes
%J International Journal of Computer Applications
%@ 0975-8887
%V 24
%N 1
%P 8-15
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

We present in this work, a new unsupervised data classification technique based on a three steps system: Split, Clean and Merge. In this system, the classes are represented by a set of subclasses that we call prototypes. The prototypes are created in an incremental way from the initial data set. No prior knowledge on the classes is required. The data are presented to the system one by one in an arbitrary way. The system built on a neural network strategy ends up by acquiring knowledge on the data and gathers the data into a set of real classes which may have a non convex structure. The method proposed is compared to the fuzzy C-means ‘FCM’ and fuzzy min max clustering ‘FMMC’ methods through a number of simulations. The results obtained by the proposed method are very good.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Unsupervised classification split clean merge convex and non convex classes fuzzy min max clustering fuzzy C-means

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