CLUSTERING UNSUPERVISED LEARNING IN LARGE BIOLOGICAL DATA

CLUSTERING UNSUPERVISED LEARNING IN LARGE BIOLOGICAL DATA 

Nabil Belacel, Christa Wang, and Miroslava Cuperlovic-Culf 

National Research Council Canada, Institute for Information Technology, 

Statistical Bioinformatics A Guide for Life and Biomedical Science Researchers. Edited by Jae K. Lee Copyright 2010 Wiley-BlackweU 

This chapter begins with a high-level overview of similarity measures followed by a discussion of the commonly used clustering approaches, including few exemplary applications in biomedical sciences. The final section of this chapter is devoted to cluster validity methods developed for measuring the compactness and separation quality of the clusters produced by the analysis. 

---