Cluster Ensembles a Knowledge Reuse Framework for Combining Partitionings
Abstract
It is widely recognized that combining multiple classification or regression models typically provides superior results compared to using a single, well-tuned model. However, there are no well known approaches to combining multiple non-hierarchical clusterings. The idea of combining cluster labelings without accessing the original features leads us to a general knowledge reuse framework that we call cluster ensembles. Our contribution in this paper is to formally define the cluster ensemble problem as an optimization problem and to propose three effective and efficient combiners for solving it based on a hypergraph model. Results on synthetic as well as real data sets are given to show that cluster ensembles can (i) improve quality and robustness, and (ii) enable distributed clustering.
Cite
Text
Strehl and Ghosh. "Cluster Ensembles a Knowledge Reuse Framework for Combining Partitionings." AAAI Conference on Artificial Intelligence, 2002. doi:10.5555/777092.777110Markdown
[Strehl and Ghosh. "Cluster Ensembles a Knowledge Reuse Framework for Combining Partitionings." AAAI Conference on Artificial Intelligence, 2002.](https://mlanthology.org/aaai/2002/strehl2002aaai-cluster/) doi:10.5555/777092.777110BibTeX
@inproceedings{strehl2002aaai-cluster,
title = {{Cluster Ensembles a Knowledge Reuse Framework for Combining Partitionings}},
author = {Strehl, Alexander and Ghosh, Joydeep},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2002},
pages = {93-99},
doi = {10.5555/777092.777110},
url = {https://mlanthology.org/aaai/2002/strehl2002aaai-cluster/}
}