@article{MAKHILLIJSC20127421086,
    title = {A New Semi-Fuzzy Algorithm for Cluster Detection and Characterization},
    journal = {International Journal of Soft Computing},
    volume = {7},
    number = {4},
    pages = {191-198},
    year = {2012},
    issn = {1816-9503},
    doi = {ijscomp.2012.191.198},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2012.191.198},
    author = {Hanane,Abdelaziz and},
    keywords = {pattern classification,fuzzy clustering,Cluster analysis,unsupervised learning,Morocco},
    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.}
    }