Link-based cluster ensembles for heterogeneous biological data analysis

Natthakan Iam-On*, Simon Garrett, Chris Price, Tossapon Boongoen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

8 Citations (SciVal)


Clinical data has been employed as the major factor for traditional cancer prognosis. However, this classic approach may be ineffective for analyzing morphologically indistinguishable tumor subtypes. As such, the microarray technology emerges as the promising alternative. Despite a large number of microarray studies, the actual clinical application of gene expression data analysis remains limited due to the complexity of generated data and the noise level. Recently, the integrative cluster analysis of both clinical and gene expression data has shown to be an effective alternative to overcome the above-mentioned problems. This paper presents a novel method for using cluster ensembles that is accurate for analyzing heterogeneous biological data. It overcomes the problem of selecting an appropriate clustering algorithm or parameter setting of any potential candidate, especially with a new set of data. The evaluation on real biological and benchmark datasets suggests that the quality of the proposed model is higher than many state-of-the-art cluster ensemble techniques and standard clustering algorithms. Also, its performance is robust to the parameter perturbation, thus providing a reliable and useful means for data analysts and bioinformaticians. Online supplementary is available at

Original languageEnglish
Title of host publicationInternational Conference on Bioinformatics and Biomedicine
Subtitle of host publicationBIBM 2010 Proceedings
PublisherIEEE Press
Number of pages6
ISBN (Electronic)978-1-4244-8307-5
ISBN (Print)9781424483075
Publication statusPublished - 04 Feb 2011
EventBioinformatics and Biomedicine - , Hong Kong
Duration: 18 Dec 201021 Dec 2010
Conference number: 2010

Publication series

NameProceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010


ConferenceBioinformatics and Biomedicine
Abbreviated titleBIBM
Country/TerritoryHong Kong
Period18 Dec 201021 Dec 2010


  • Cluster ensembles
  • Clustering
  • Heterogeneous biological data
  • Link analysis


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