Intractability and Clustering with Constraints
Abstract
Clustering with constraints is a developing area of machine learning. Various papers have used constraints to enforce particular clusterings, seed clustering algorithms and even learn distance functions which are then used for clustering. We present intractability results for some constraint combinations and illustrate both formally and experimentally the implications of these results for using constraints with clustering.
Cite
Text
Davidson and Ravi. "Intractability and Clustering with Constraints." International Conference on Machine Learning, 2007. doi:10.1145/1273496.1273522Markdown
[Davidson and Ravi. "Intractability and Clustering with Constraints." International Conference on Machine Learning, 2007.](https://mlanthology.org/icml/2007/davidson2007icml-intractability/) doi:10.1145/1273496.1273522BibTeX
@inproceedings{davidson2007icml-intractability,
title = {{Intractability and Clustering with Constraints}},
author = {Davidson, Ian and Ravi, S. S.},
booktitle = {International Conference on Machine Learning},
year = {2007},
pages = {201-208},
doi = {10.1145/1273496.1273522},
url = {https://mlanthology.org/icml/2007/davidson2007icml-intractability/}
}