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Artículo
Kernel Penalized K-means: A feature selection method based on Kernel K-means
(ELSEVIER SCIENCE BV, 2015-11-20)
We present an unsupervised method that selects the most relevant features using an embedded strategy while maintaining the cluster structure found with the initial feature set. It is based on the idea of simultaneously ...
Artículo
New heuristic for harmonic means clustering
(Springer, 2014-05-06)
It is well known that some local search heuristics for K-clustering problems, such as k-means heuristic for minimum sum-of-squares clustering occasionally stop at a solution with a smaller number of clusters than the ...
Artículo
Sum-of-squares clustering on networks
(University of Belgrade, 2011)
Finding p prototypes by minimizing the sum of the squared distances from a set of points to its closest prototype is a well-studied problem in clustering, data analysis and continuous location. In this note, this very ...
Artículo
Clustering categories in support vector machines
(Elsevier, 2016-02)
The support vector machine (SVM) is a state-of-the-art method in supervised classification. In this paper the Cluster Support Vector Machine (CLSVM) methodology is proposed with the aim to increase the sparsity of the SVM ...