Hanane  Benrachid, Abdelaziz  Bouroumi, Rkia  Fajr, 
    A New Semi-Fuzzy Algorithm for Cluster Detection and Characterization,
    International Journal of Soft Computing,
    Volume 7,Issue 4,
    2012,
    Pages 191-198,
    ISSN 1816-9503,
    ijscomp.2012.191.198,
    (https://makhillpublications.co/view-article.php?doi=ijscomp.2012.191.198)
    Abstract: 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.
    Keywords: pattern classification;fuzzy clustering;Cluster analysis;unsupervised learning;Morocco