Cost functions for pairwise data clustering
Disordered Systems and Neural Networks
2009-11-07 v1
Abstract
Cost functions for non-hierarchical pairwise clustering are introduced, in the probabilistic autoencoder framework, by the request of maximal average similarity between the input and the output of the autoencoder. The partition provided by these cost functions identifies clusters with dense connected regions in data space; differences and similarities with respect to a well known cost function for pairwise clustering are outlined.
Cite
@article{arxiv.cond-mat/0103414,
title = {Cost functions for pairwise data clustering},
author = {L. Angelini and L. Nitti and M. Pellicoro and S. Stramaglia},
journal= {arXiv preprint arXiv:cond-mat/0103414},
year = {2009}
}
Comments
5 pages, 4 figures