Hierarchical Constraints - Providing Structural Bias for Hierarchical Clustering
Abstract
Constrained clustering received a lot of attention in the last years. However, the widely used pairwise constraints are not generally applicable for hierarchical clustering, where the goal is to derive a cluster hierarchy instead of a flat partition. Therefore, we propose for the hierarchical setting—based on the ideas of pairwise constraints—the use of must-link-before (MLB) constraints. In this paper, we discuss their properties and present an algorithm that is able to create a hierarchy by considering these constraints directly. Furthermore, we propose an efficient data structure for its implementation and evaluate its effectiveness with different datasets in a text clustering scenario.
Cite
Text
Bade and Nürnberger. "Hierarchical Constraints - Providing Structural Bias for Hierarchical Clustering." Machine Learning, 2014. doi:10.1007/S10994-013-5397-9Markdown
[Bade and Nürnberger. "Hierarchical Constraints - Providing Structural Bias for Hierarchical Clustering." Machine Learning, 2014.](https://mlanthology.org/mlj/2014/bade2014mlj-hierarchical/) doi:10.1007/S10994-013-5397-9BibTeX
@article{bade2014mlj-hierarchical,
title = {{Hierarchical Constraints - Providing Structural Bias for Hierarchical Clustering}},
author = {Bade, Korinna and Nürnberger, Andreas},
journal = {Machine Learning},
year = {2014},
pages = {371-399},
doi = {10.1007/S10994-013-5397-9},
volume = {94},
url = {https://mlanthology.org/mlj/2014/bade2014mlj-hierarchical/}
}