In this paper we propose the Possibilistic C-Means in Feature Space and the One-Cluster Possibilistic C-Means in Feature Space algorithms which are kernel methods for clustering in...
Maurizio Filippone, Francesco Masulli, Stefano Rov...
A unified variational methodology is developed for classification and clustering problems, and tested in the classification of tumors from gene expression data. It is based on flu...
J. P. Agnelli, M. Cadeiras, E. G. Tabak, C. V. Tur...
We propose a novel approach to image segmentation, called feature and spatial domain clustering. The method is devised to group pixel data by taking into account simultaneously bo...
Abstract. This paper presents a simple, yet effective method of building a codebook for pairs of spatially close SIFT descriptors. Integrating such codebook into the popular bag-o...
In this paper we propose and test the use of hierarchical clustering for feature selection. The clustering method is Ward's with a distance measure based on GoodmanKruskal ta...