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• W1577771858 abstract "A new simulation optimization technique that extends the pure nested partition (NP) algorithm is presented in this thesis. This method is called the nested partition with inheritance. Furthermore, a statistical selection method and a random search method are introduced to overcome certain shortcomings of the pure NP (Nested Partitions) algorithm. Finally, the suggested algorithms are applied to a data mining problems, namely data clustering. The basic idea of a NP algorithm is very simple. At each iteration, the most promising region is partitioned and the performance of the partitioned region is evaluated using sampling. Based on the performance evaluation, the most promising region is chosen for the next iteration. These procedures are repeated until it satisfies the termination condition. Even though the pure NP method guarantees the convergence to the optimal solution, it has two apparent problems. The first problem is related to the two sources of errors in the estimate of each region during the sampling phase of the NP algorithm: the sampling error due to the use of a limited number of sample points from the region and the estimation error due to the use of optimization under uncertainty. The other problem is that at each iteration, there is no guarantee of whether the correct move is made or not. To handle these shortcomings, two extensions to the pure NP are suggested. To rigorously determine the required sample effort, some statistical selection methods are implemented, which include the Nelson Matejcik procedure, the Rinott procedure, and the Dudewicz and Dalai procedure, as" @default.
• W1577771858 created "2016-06-24" @default.
• W1577771858 creator A5000774459 @default.
• W1577771858 date "2018-08-13" @default.
• W1577771858 modified "2023-10-16" @default.
• W1577771858 title "Optimization under uncertainty with application to data clustering" @default.
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