RCE Rapid Centroid Estimation (RCE) clustering (2014): A semi-stochastic lightweight clustering algorithm using particle swarm.
% An example run of the algorithm using Iris Dataset
% load iris dataset
X = irisInputs;
N = size(irisInputs,2);
% set the fuzzifier constant to 1.4
m = 1.4;
% Optimize the swarm using 80% resampling rate and mahalanobis distance
swarm = RCE(X, 3, 'distance','mahalanobis','fuzzifier',m, 'display','text', ...
'swarm',6, 'subsprob',0.03, 'maxiter',100,'resampling_rate',0.8,'calculate_labels', false);
% calculate the fuzzy labels, crisp labels, and numeric labels from the
% input vectors using the Swarm
[softlabels, crisplabels, numlabels] = swarm_cluster(X,swarm);
% plot the fuzzy voronoi cells on the 1st and 3rd dimension
% Perform fuzzy evidence accumulation on the swarm
ensemble = EnsembleAggregate(softlabels,'average',true);
% plot the scatter matrix
RCE is proposed as a derivate of PSC algorithm with radically reduced time complexity. The quality of the results produced are similar to PSC. The advantage of RCE is RCE has lesser complexity, faster convergence, and reduced likelihood of suboptimal convergence [1-2]. Recent updates to RCE removes the redundancies of RCE and further decrease the overall complexity, enabling it to perform Ensemble Clustering in quasilinear complexity. The additional algorithms are Tsaipei-Wang's co-association tree (CA-tree) and Fuzzy Evidence Accumulation. The code for CA-tree will be provided in the next update.
 M. Yuwono, S.W. Su, B. Moulton, & H. Nguyen, "Data Clustering Using
Variants of Rapid Centroid Estimation", IEEE Transactions on
Evolutionary Computation, Vol 18, no.3, pp.366-377. ISSN:1089-778X.
 M. Yuwono, S.W. Su, B. Moulton, H. Nguyen, "An Algorithm for Scalable
Clustering: Ensemble Rapid Centroid Estimation", in Proc 2014 IEEE
Congress on Evolutionary Computation, 2014, pp.1250-1257.
Copyright 2011-2014 Mitchell Yuwono.
$Revision: 1.2.0 $ $Date: 2014/11/10 18:02:00 $