Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Cluster analysis of gene expression data
Cluster analysis of gene expression data
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Hi-index | 0.00 |
Clustering analysis is an important exploratory tool that aids in the analysis and organization of genomic data. Each biological data set has different characteris, and the decision of which clustering method is appropriate and how many clusters are optimal on a dataset-by-dataset basis can be problematic. The Figure of Merit (FOM) is a quantitative clustering validation method designed to aid in these decisions. While FOM is useful, it does have limitations which must be considered when using it. This research shows that the FOM is biased toward Euclidean distance. Performing FOM analysis on clusters created by using Pearson's correlation coefficient as a similarity measure is shown to be non-optimal, and mathematically inadvisable. A new, correlation coefficient-biased version of the FOM has been developed, and preliminary results indicate that this new FOM is effectively biased toward clusters generated using the correlation coefficient.