Semi-supervised Distributed Clustering for Bioinformatics - Comparison Study

Huayiing Li, Aleksandar Jeremic

2017

Abstract

Clustering analysis is a widely used technique in bioinformatics and biochemistry for variety of applications such as detection of new cell types, evaluation of drug response, etc. Since different applications and cells may require different clustering algorithms combining multiple clustering results into a consensus clustering using distributed clustering is a popular and efficient method to improve the quality of clustering analysis. Currently existing solutions are commonly based on supervised techniques which do not require any a priori knowledge. However in certain cases, a priori information on particular labelings may be available a priori. In these cases it is expected that performance improvement can be achieved by utilizing this prior information. To this purpose in this paper, we propose two semi-supervised distributed clustering algorithms and evaluate their performance for different base clusterings

Paper Citation

in Harvard Style

Li H. and Jeremic A. (2017). Semi-supervised Distributed Clustering for Bioinformatics - Comparison Study. In - BIOSIGNALS, (BIOSTEC 2017) ISBN , pages 0-0. DOI: 10.5220/0006253500001488

in Bibtex Style

@conference{biosignals17,
author={Huayiing Li and Aleksandar Jeremic},
title={Semi-supervised Distributed Clustering for Bioinformatics - Comparison Study},
booktitle={ - BIOSIGNALS, (BIOSTEC 2017)},
year={2017},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006253500001488},
isbn={},
}

in EndNote Style

TY - CONF

JO - - BIOSIGNALS, (BIOSTEC 2017)
TI - Semi-supervised Distributed Clustering for Bioinformatics - Comparison Study
SN -
AU - Li H.
AU - Jeremic A.
PY - 2017
SP - 0
EP - 0
DO - 10.5220/0006253500001488