Constrained Clustering with Local Constraint Propagation
Abstract
We consider the problem of multi-class constrained clustering given pairwise constraints, which specify the pairs of data belonging to the same or different clusters. In this paper, we present a new constrained clustering algorithm, Local Constraint Propagation (LCP), which can propagate the influence of each pairwise constraint to the unconstrained data with sufficient smoothness. It not only reveals the underlying structures of the clusters, but also integrates the influence of all the pairwise constraints on every data point. Promising experiments on image segmentations demonstrate the effectiveness of our method.
Cite
Text
He et al. "Constrained Clustering with Local Constraint Propagation." European Conference on Computer Vision Workshops, 2012. doi:10.1007/978-3-642-33885-4_23Markdown
[He et al. "Constrained Clustering with Local Constraint Propagation." European Conference on Computer Vision Workshops, 2012.](https://mlanthology.org/eccvw/2012/he2012eccvw-constrained/) doi:10.1007/978-3-642-33885-4_23BibTeX
@inproceedings{he2012eccvw-constrained,
title = {{Constrained Clustering with Local Constraint Propagation}},
author = {He, Ping and Xu, Xiao-hua and Chen, Ling},
booktitle = {European Conference on Computer Vision Workshops},
year = {2012},
pages = {223-232},
doi = {10.1007/978-3-642-33885-4_23},
url = {https://mlanthology.org/eccvw/2012/he2012eccvw-constrained/}
}