International Journal of Soft Computing
ISSN: OnlineISSN: Print 1816-9503
Abstract
Researchers propose a new algorithm for detecting homogeneous clusters within
sets of unlabeled objects represented by numerical data of the form X = {x1,
x2,..., xn} 
.
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 = {v1,
v2,..., vn}
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.
How to cite this article:
Hanane Benrachid, Abdelaziz Bouroumi and Rkia Fajr. A New Semi-Fuzzy Algorithm for Cluster Detection and Characterization.
DOI: https://doi.org/10.36478/ijscomp.2012.191.198
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2012.191.198