New Cluster Detection using Semi-supervised Clustering Ensemble Method

Huaying Li, Aleksandar Jeremic

2018

Abstract

In the recent years there has been tremendous development of data acquisition system resulting in a whole new set of so called big data problems. Since these data structures are inherently dynamic and constantly changing the number of clusters is usually unknown. Furthermore the ”true” number of clusters can depend on the constraints and/or perception (biases) set by experts, users, customers, etc., which can also change. In this paper we propose a new cluster detection algorithm based on a semi-supervised clustering ensemble method. Information fusion techniques have been widely applied in many applications including clustering, classification, detection, etc. Although clustering is unsupervised and it does not require any training data, in many applications, expert opinions are usually available to label a portion of data observations. These labels can be viewed as the guidance information to combine the cluster labels that are generated by different local clusters. It consists of two major steps: the base clustering generation and the fusion. Since the step of generating base clusterings is unsupervised and the step of combining base clusterings is supervised, in the context of this paper, we name the algorithm as the semi-supervised clustering ensemble algorithm. We then propose to detect a new cluster utilizing the average association vector computed for each data point by the semi-supervised method.

Paper Citation

in Harvard Style

Li H. and Jeremic A. (2018). New Cluster Detection using Semi-supervised Clustering Ensemble Method. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS; ISBN 978-989-758-279-0, SciTePress, pages 221-226. DOI: 10.5220/0006653802210226

in Bibtex Style

@conference{biosignals18,
author={Huaying Li and Aleksandar Jeremic},
title={New Cluster Detection using Semi-supervised Clustering Ensemble Method},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS},
year={2018},
pages={221-226},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006653802210226},
isbn={978-989-758-279-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 4: BIOSIGNALS
TI - New Cluster Detection using Semi-supervised Clustering Ensemble Method
SN - 978-989-758-279-0
AU - Li H.
AU - Jeremic A.
PY - 2018
SP - 221
EP - 226
DO - 10.5220/0006653802210226
PB - SciTePress