Evaluation of Topographic Clustering and Its Kernelization
Abstract
We consider the topographic clustering task and focus on the problem of its evaluation, which enables to perform model selection: topographic clustering algorithms, from the original Self Organizing Map to its extension based on kernel (STMK), can be viewed in the unified framework of constrained clustering. Exploiting this point of view, we discuss existing quality measures and we propose a new criterion based on an F-measure, which combines a compacity with an organization criteria and extend it to their kernel-based version.
Cite
Text
Lesot et al. "Evaluation of Topographic Clustering and Its Kernelization." European Conference on Machine Learning, 2003. doi:10.1007/978-3-540-39857-8_25Markdown
[Lesot et al. "Evaluation of Topographic Clustering and Its Kernelization." European Conference on Machine Learning, 2003.](https://mlanthology.org/ecmlpkdd/2003/lesot2003ecml-evaluation/) doi:10.1007/978-3-540-39857-8_25BibTeX
@inproceedings{lesot2003ecml-evaluation,
title = {{Evaluation of Topographic Clustering and Its Kernelization}},
author = {Lesot, Marie-Jeanne and d'Alché-Buc, Florence and Siolas, George},
booktitle = {European Conference on Machine Learning},
year = {2003},
pages = {265-276},
doi = {10.1007/978-3-540-39857-8_25},
url = {https://mlanthology.org/ecmlpkdd/2003/lesot2003ecml-evaluation/}
}