Multidimensional Scaling and Data Clustering
Abstract
Visualizing and structuring pairwise dissimilarity data are difficult combinatorial op(cid:173) timization problems known as multidimensional scaling or pairwise data clustering. Algorithms for embedding dissimilarity data set in a Euclidian space, for clustering these data and for actively selecting data to support the clustering process are discussed in the maximum entropy framework. Active data selection provides a strategy to discover structure in a data set efficiently with partially unknown data.
Cite
Text
Hofmann and Buhmann. "Multidimensional Scaling and Data Clustering." Neural Information Processing Systems, 1994.Markdown
[Hofmann and Buhmann. "Multidimensional Scaling and Data Clustering." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/hofmann1994neurips-multidimensional/)BibTeX
@inproceedings{hofmann1994neurips-multidimensional,
title = {{Multidimensional Scaling and Data Clustering}},
author = {Hofmann, Thomas and Buhmann, Joachim},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {459-466},
url = {https://mlanthology.org/neurips/1994/hofmann1994neurips-multidimensional/}
}