Information Fusion for Semi-supervised Cluster Labelings

Huaying Li, Aleksandar Jeremic

2015

Abstract

Clustering analysis is a widely used technique to find hidden patterns of a data set. Combining multiple clustering results into a consensus clustering (cluster ensemble) is a popular and efficient method to improve the quality of clustering analysis. Many algorithms were proposed in the literature and most of which are unsupervised learning techniques. In this paper, we proposed a semi-supervised cluster ensemble algorithm. It is so-called semi-supervised because labels of some data points in the given data set are known or provided by experts. To evaluate the performance of the proposed algorithm, we compare it with other well-known algorithms, such as MCLA and BCE.

References

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Paper Citation


in Harvard Style

Li H. and Jeremic A. (2015). Information Fusion for Semi-supervised Cluster Labelings . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 318-323. DOI: 10.5220/0005282203180323


in Bibtex Style

@conference{biosignals15,
author={Huaying Li and Aleksandar Jeremic},
title={Information Fusion for Semi-supervised Cluster Labelings},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)},
year={2015},
pages={318-323},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005282203180323},
isbn={978-989-758-069-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)
TI - Information Fusion for Semi-supervised Cluster Labelings
SN - 978-989-758-069-7
AU - Li H.
AU - Jeremic A.
PY - 2015
SP - 318
EP - 323
DO - 10.5220/0005282203180323