When Is Constrained Clustering Beneficial, and Why?
Abstract
Several researchers have illustrated that constraints can improve the results of a variety of clustering algorithms. However, there can be a large variation in this improvement, even for a fixed number of constraints for a given data set. We present the first attempt to provide insight into this phenomenon by characterizing two constraint set properties: inconsistency and incoherence. We show that these measures are strongly anti-correlated with clustering algorithm performance. Since they can be computed prior to clustering, these measures can aid in deciding which constraints to use in practice.
Cite
Text
Wagstaff et al. "When Is Constrained Clustering Beneficial, and Why?." AAAI Conference on Artificial Intelligence, 2006.Markdown
[Wagstaff et al. "When Is Constrained Clustering Beneficial, and Why?." AAAI Conference on Artificial Intelligence, 2006.](https://mlanthology.org/aaai/2006/wagstaff2006aaai-constrained/)BibTeX
@inproceedings{wagstaff2006aaai-constrained,
title = {{When Is Constrained Clustering Beneficial, and Why?}},
author = {Wagstaff, Kiri and Basu, Sugato and Davidson, Ian},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2006},
url = {https://mlanthology.org/aaai/2006/wagstaff2006aaai-constrained/}
}