* fzGASCE: A fuzzy clustering method using Genetic Algorithm and Fuzzy Subtractive Clustering.
  We present fzGASCE, a novel fuzzy clustering algorithm that combines the Genetic Algorithm with the fuzzy subtractive clustering and Bayesian based cluster evaluation methods to address the problem of data clustering without knowing the exact number of clusters. fzGASCE outperformed other methods on both artificial and real datasets, particularly on datasets with clusters that differed in size, not only in detecting the number of clusters but also in grouping the data points into their own clusters. fzGASCE 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 Information and Knowledge Engineering, IKE'12, July 18 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