Arı, ÇağlarAksoy, Selim2016-02-082016-02-082010-08http://hdl.handle.net/11693/28513Date of Conference: 23-26 Aug. 2010Conference name: 20th International Conference on Pattern Recognition, 2010We present solutions to two problems that prevent the effective use of population-based algorithms in clustering problems. The first solution presents a new representation for arbitrary covariance matrices that allows independent updating of individual parameters while retaining the validity of the matrix. The second solution involves an optimization formulation for finding correspondences between different parameter orderings of candidate solutions. The effectiveness of the proposed solutions are demonstrated on a novel clustering algorithm based on particle swarm optimization for the estimation of Gaussian mixture models. © 2010 IEEE.EnglishCandidate solutionClustering problemsCovariance matricesGaussian mixture modelMatrixOptimization formulationsPopulation-based algorithmBlind source separationClustering algorithmsCovariance matrixOptical communicationParticle swarm optimization (PSO)Pattern recognitionMaximum likelihood estimationConference Paper10.1109/ICPR.2010.188