* fzPBD: Probability based method for defuzzification of fuzzy cluster partition.
  We present fzPBD, a novel defuzzification algorithm that uses a Bayesian based approach to generate a probabilistic model for the given fuzzy partition and applies the model to produce classification information for data objects in the dataset. fzPBD outperformed other methods on both artificial and real datasets, particularly on datasets with clusters that differed in size. fzPBD is therefore appropriate for real-world datasets where the data densities are not uniformly distributed.
 
  Online demo This online demo was developed using AJAX technology, PHP and C++ programming languages.
  Presentation slides These slides were presented at the WorldComp12 conferences on Artificial Intelligence, ICAI'12, July 19 2012, in Las Vegas NV, USA.
 
  Artificial datasets This package contains two artificial datasets generated using the method of Xu and Jordan (1996).
 
  Real datasets This package contains two datasets: iris, wine from UCI ML Repository