Algorithms with Greedy Heuristic Procedures for Mixture Probability Distribution Separation - Kazakovtsev, Lev; Stashkov, Dmitry; Gudyma, Mikhail; Kazakovtsev, Vladimir (Yugoslav Journal of Operations Research; Vol 29 No 1 (2019), 2018)
We propose new evolutionary algorithms with greedy agglomerative heuristic procedures for fuzzy clustering problem based on model of mixture probability distribution separation. New algorithms use the EM algorithm (Expectation Maximization) for local search and ideas of genetic evolutionary algorithms combined with greedy agglomerative heuristic procedures as global search strategy. This combination allows to obtain better results with higher values of the log likelihood objective function in comparison with the EM algorithm and its modification. Results of testing of the electronic components shipped for the space industry are represented by arrays of data vectors of very high dimensionality, up to hundreds of dimensions. One of the most important problems for increasing the quality of the electronic units is detection of the homogeneous production batches of the electronic devices (components). The EM algorithm and its modifications do not guarantee obtaining optimal or near optimal results with highest log likelihood values for such problems. Computational experiments with electronic component testing data and classical datasets for fuzzy clustering problems show that this new algorithm allows obtaining more precise results in comparison known algorithms.