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In this paper, the method of selecting cluster number is proposed using finite mixture modeling algorithm and penalized Fisher class separability measure ...
Abstract—It is well known that classical clustering algo- rithms have a problem of determining an appropriate number of clusters. In this paper, the method ...
A novel hierarchical clustering algorithm is proposed, where the number of clusters is optimally determined according to the Fisher class separability ...
The iterative optimization strategy is designed using Finite mixture model and penalized Fisher class separability measure. The parameterized architecture of ...
Abstract: Finite mixture models have a long history in statistics, hav- ing been used to model population heterogeneity, generalize distributional.
Missing: Separability | Show results with:Separability
Abstract—We consider the clustering problem of di- rectional data and specifically the choice of the num- ber of clusters. Setting this problem under the ...
A genetic algorithm for mixture model clustering using variable data segmentation and model selection is proposed in this study.
We propose a selection rule that allows choosing among many clustering solutions, eventually obtained from different methods.
Missing: Penalized Separability
Aug 26, 2014 · Abstract In the framework of Bayesian model-based clus- tering based on a finite mixture of Gaussian distributions, we.
The main topic of this work is the choice of the number of classes in the model- based clustering framework, and then the choice of the number of components of ...
Missing: Separability | Show results with:Separability