TY  - JOUR
T1  - A New Semi-Fuzzy Algorithm for Cluster Detection and Characterization
AU - Benrachid, Hanane AU - Bouroumi, Abdelaziz AU - Fajr, Rkia 
JO  - International Journal of Soft Computing
VL  - 7
IS  - 4
SP  - 191
EP  - 198
PY  - 2012
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2012.191.198
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2012.191.198
KW  - pattern classification
KW  -fuzzy clustering
KW  -Cluster analysis
KW  -unsupervised learning
KW  -Morocco
AB  - Researchers propose a new algorithm for detecting homogeneous clusters within 
sets of unlabeled objects represented by numerical data of the form X = {x<SUB>1</SUB>, 
x<SUB>2</SUB>,..., x<SUB>n</SUB>} <img src="http://docsdrive.com/images/medwelljournals/ijscomp/2012/img1-2k12-191-198.gif" width="27" height="13" align="absmiddle">. 
By quickly exploring the available data using an inter-objects similarity measure 
plus an ambiguity measure of individual objects, this algorithm provides the number 
of clusters present in X, plus a set of optimized prototypes V = {v<SUB>1</SUB>, 
v<SUB>2</SUB>,..., v<SUB>n</SUB>} <img src="http://docsdrive.com/images/medwelljournals/ijscomp/2012/img1-2k12-191-198.gif" width="27" height="13" align="absmiddle"> 
where each prototype characterizes one of the c detected clusters. The performance 
of the algorithm is illustrated by typical examples of simulation results obtained 
for different real test data.
ER  -