Authors:
Huaying Li
and
Aleksandar Jeremic
Affiliation:
McMaster University, Canada
Keyword(s):
Clustering, Information Fusion, Cluster Ensemble and Semi-supervised Learning.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Multimedia
;
Multimedia Signal Processing
;
Pattern Recognition
;
Telecommunications
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.