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- W2529503886 abstract "In this paper, a new algorithm is presented for unsupervised learning of Finite Mixture Models using incomplete data set. This algorithm applies Particle Swarm Optimization to solve the local optima problem of the Expectation-Maximization algorithm. In addition, the proposed algorithm uses Locally-tuned General Regression neural networks with Optimal Completion Strategy to estimate missing values in the input data set. A comparison study shows the superiority of the proposed algorithm over other algorithms in the literature in unsupervised learning of FMM parameters that produce minimum mis-classification errors when used in clustering incomplete data." @default.
- W2529503886 created "2016-10-14" @default.
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- W2529503886 date "2011-12-11" @default.
- W2529503886 modified "2023-09-24" @default.
- W2529503886 title "Using Particle Swarm Optimization and Locally-Tuned General Regression Neural Networks with Optimal Completion for Clustering Incomplete Data Using Finite Mixture Models" @default.
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