Display Settings:

Format

Send to:

Choose Destination
J Biomed Inform. 2013 Jun;46(3):436-43. doi: 10.1016/j.jbi.2013.02.001. Epub 2013 Feb 27.

Complementary ensemble clustering of biomedical data.

Author information

  • 1Yale University School of Medicine, Yale University, New Haven, CT 06520, USA. samah.fodeh@yale.edu

Abstract

The rapidly growing availability of electronic biomedical data has increased the need for innovative data mining methods. Clustering in particular has been an active area of research in many different application areas, with existing clustering algorithms mostly focusing on one modality or representation of the data. Complementary ensemble clustering (CEC) is a recently introduced framework in which Kmeans is applied to a weighted, linear combination of the coassociation matrices obtained from separate ensemble clustering of different data modalities. The strength of CEC is its extraction of information from multiple aspects of the data when forming the final clusters. This study assesses the utility of CEC in biomedical data, which often have multiple data modalities, e.g., text and images, by applying CEC to two distinct biomedical datasets (PubMed images and radiology reports) that each have two modalities. Referent to five different clustering approaches based on the Kmeans algorithm, CEC exhibited equal or better performance in the metrics of micro-averaged precision and Normalized Mutual Information across both datasets. The reference methods included clustering of single modalities as well as ensemble clustering of separate and merged data modalities. Our experimental results suggest that CEC is equivalent or more efficient than comparable Kmeans based clustering methods using either single or merged data modalities.

Copyright © 2013 Elsevier Inc. All rights reserved.

PMID:
23454721
[PubMed - indexed for MEDLINE]
PMCID:
PMC4007219
Free PMC Article

Images from this publication.See all images (5)Free text

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science Icon for PubMed Central
    Loading ...
    Write to the Help Desk