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Among neighboring models, the one with the minimum sum of squared errors – on certain time intervals – replaces the current model.
It can also be used to search for the optimum estimator parameters to give the minimum sum of variances.
The goal of the process design is to select the structure and parameters to give the minimum sum of the depreciation and operating cost.
The integrability criterion for distillation columns sequence was defined as the minimum sum of products of intracolumn temperatures and their heat flow rates (Min-criterion).
Harmonic means clustering is a variant of minimum sum of squares clustering (which is sometimes called K-means clustering), designed to alleviate the dependance of the results on the choice of the initial solution.
To provide a suitable case study, we introduce a new data granulation technique based on the minimum sum of distances, which is designed to generate type-2 fuzzy sets.
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This paper introduces an algorithm for solving the minimum sum-of-squares clustering problems using their difference of convex representations.
A series of metaheuristic algorithms is proposed and analyzed for the non-hierarchical clustering problem under the criterion of minimum sum-of-squares clustering.
The aim of this paper is to design an algorithm based on nonsmooth optimization techniques to solve the minimum sum-of-squares clustering problems in very large data sets.
Merz and Zell [ 27] proposed a memetic algorithm for the problem, formulated as finding the minimum sum-of-squares clustering [ 28, 29].
In the first method, we define the representative to be a time series that has the minimum sum-of-distances (MSD) with all the other time series in that cluster.
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