Central and Pairwise Data Clustering by Competitive Neural Networks
Abstract
Data clustering amounts to a combinatorial optimization problem to re(cid:173) duce the complexity of a data representation and to increase its precision. Central and pairwise data clustering are studied in the maximum en(cid:173) tropy framework. For central clustering we derive a set of reestimation equations and a minimization procedure which yields an optimal num(cid:173) ber of clusters, their centers and their cluster probabilities. A meanfield approximation for pairwise clustering is used to estimate assignment probabilities. A se1fconsistent solution to multidimensional scaling and pairwise clustering is derived which yields an optimal embedding and clustering of data points in a d-dimensional Euclidian space.
Cite
Text
Buhmann and Hofmann. "Central and Pairwise Data Clustering by Competitive Neural Networks." Neural Information Processing Systems, 1993.Markdown
[Buhmann and Hofmann. "Central and Pairwise Data Clustering by Competitive Neural Networks." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/buhmann1993neurips-central/)BibTeX
@inproceedings{buhmann1993neurips-central,
title = {{Central and Pairwise Data Clustering by Competitive Neural Networks}},
author = {Buhmann, Joachim and Hofmann, Thomas},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {104-111},
url = {https://mlanthology.org/neurips/1993/buhmann1993neurips-central/}
}