Unsupervised Consensus Functions Applied to Ensemble Biclustering
Blaise Hanczar, Mohamed Nadif
2014
Abstract
The ensemble methods are very popular and can improve significantly the performance of classification and clustering algorithms. Their principle is to generate a set of different models, then aggregate them into only one. Recent works have shown that this approach can also be useful in biclustering problems.The crucial step of this approach is the consensus functions that compute the aggregation of the biclusters. We identify the main consensus functions commonly used in the clustering ensemble and show how to extend them in the biclustering context. We evaluate and analyze the performances of these consensus functions on several experiments based on both artificial and real data.
DownloadPaper Citation
in Harvard Style
Hanczar B. and Nadif M. (2014). Unsupervised Consensus Functions Applied to Ensemble Biclustering . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 30-39. DOI: 10.5220/0004789800300039
in Bibtex Style
@conference{icpram14,
author={Blaise Hanczar and Mohamed Nadif},
title={Unsupervised Consensus Functions Applied to Ensemble Biclustering},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={30-39},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004789800300039},
isbn={978-989-758-018-5},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Unsupervised Consensus Functions Applied to Ensemble Biclustering
SN - 978-989-758-018-5
AU - Hanczar B.
AU - Nadif M.
PY - 2014
SP - 30
EP - 39
DO - 10.5220/0004789800300039