* fzBLE: A new validation method for fuzzy clustering of gene expression data.
  We present a novel method, fzBLE to evaluate results of fuzzy partitioning by the standard FCM algorithm. fzBLE is novel in that it uses the log likelihood estimator with a Bayesian model and the possibility, rather than the probability, distribution model of the dataset from the fuzzy partition. By using the Central Limit Theorem, fzBLE effectively represents distributions in real datasets. Results have shown that fzBLE performs effectively on both artificial and real datasets.
  Presentation This will be presented at the BIOCOMP 2011 conference, the World Congress in Computer Science, Computer Engineering, and Applied Computing (July 18-21, 2011, Las Vegas, Nervada USA), scheduled on July 18th 2011, at 01:40 - 02:00pm.
  Online demo This online demo was developed using AJAX technology, PHP and C++ programming languages.
 
  Artificial datasets This package contains 84 artificial datasets generated using the method of Xu and Jordan (1996).
 
  Real datasets This package contains three datasets: iris, wine and glass.
 
  Gene expression datasets This package contains three gene expression datasets: yeast, yeast-MIPS and RCNS.
 
  Artificial dataset results This package contains the performance results of fzBLE and the other cluster indices on 84 artificial datasets. This is reported in Table 1 of the manuscript.
 
  Real dataset results This package contains the performance results of fzBLE and the other cluster indices on the three datasets: iris, wine and glass. This is reported in Tables 2-4 of the manuscript.
 
  Gene expression data results This package contains the performance results of fzBLE and the other cluster indices on the three gene expression datasets: yeast, yeast-MIPS and RCNS. This is reported in Tables 5-7 of the manuscript.