This review paper aims to familiarize the reader with the battery of …

• Abstract
• Introduction
• Background
• Survey of clustering-validation techniques
• Measure-specific biases
• Guidelines for effective cluster validation
• Sample application
• Conclusion
• Acknowledgments
• References
• Figures

Biology Articles » Bioinformatics » Computational cluster validation in post-genomic data analysis » Acknowledgments

Acknowledgments

- Computational cluster validation in post-genomic data analysis

 
The authors would like to thank Oliver Sander and Roy Goodacre for proofreading and valuable feedback. J.H. acknowledges support of a scholarship by the Gottlieb Daimler- and Karl Benz-Foundation. J.K. is supported by a BBSRC David Phillips Fellowship. D.B.K. would like to thank the BBSRC, EPSRC, NERC and RSC for financial support.

Conflict of Interest: none declared.

Footnotes
¹Notable exceptions include classic expectation–maximization (EM) algorithms as well as some newly developed methods such as adaptive quality-based clustering (DeSmet et al., 2002), the self-organizing tree algorithm (Herrero et al., 2001), and multiobjective clustering (Handl and Knowles, 2005). 

²SD refers to the fact that this index measures the scattering and the distance of clusters. 

Received on March 24, 2005; revised on May 24, 2005; accepted on May 24, 2005