Dynamic Feature Selection in Incremental Hierarchical Clustering
Abstract
Feature selection has received a lot of attention in the machine learning community, but mainly under the supervised paradigm. In this work we study the potential benefits of feature selection in hierarchical clustering tasks. Particularly we address this problem in the context of incremental clustering, following the basic ideas of Gennari [ 8 ]. By using a simple implementation, we show that a feature selection scheme running in parallel with the learning process can improve the clustering task under the dimensions of accuracy, efficiency in learning, efficiency in prediction and comprehensibility.
Cite
Text
Talavera. "Dynamic Feature Selection in Incremental Hierarchical Clustering." European Conference on Machine Learning, 2000. doi:10.1007/3-540-45164-1_40Markdown
[Talavera. "Dynamic Feature Selection in Incremental Hierarchical Clustering." European Conference on Machine Learning, 2000.](https://mlanthology.org/ecmlpkdd/2000/talavera2000ecml-dynamic/) doi:10.1007/3-540-45164-1_40BibTeX
@inproceedings{talavera2000ecml-dynamic,
title = {{Dynamic Feature Selection in Incremental Hierarchical Clustering}},
author = {Talavera, Luis},
booktitle = {European Conference on Machine Learning},
year = {2000},
pages = {392-403},
doi = {10.1007/3-540-45164-1_40},
url = {https://mlanthology.org/ecmlpkdd/2000/talavera2000ecml-dynamic/}
}