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Authors: Blaise Hanczar and Mohamed Nadif

Affiliation: University Paris Descartes, France

Keyword(s): Biclustering, Ensemble Methods, Consensus Functions.

Related Ontology Subjects/Areas/Topics: Clustering ; Ensemble Methods ; Pattern Recognition ; Theory and Methods

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.

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Paper citation in several formats:
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 - ICPRAM; ISBN 978-989-758-018-5; ISSN 2184-4313, SciTePress, pages 30-39. DOI: 10.5220/0004789800300039

@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 - ICPRAM},
year={2014},
pages={30-39},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004789800300039},
isbn={978-989-758-018-5},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Unsupervised Consensus Functions Applied to Ensemble Biclustering
SN - 978-989-758-018-5
IS - 2184-4313
AU - Hanczar, B.
AU - Nadif, M.
PY - 2014
SP - 30
EP - 39
DO - 10.5220/0004789800300039
PB - SciTePress

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