* fzPBI: Probability-based imputation method for fuzzy cluster analysis of gene expression microarray data.
  We present fzPBI, a novel imputation method for fuzzy cluster analysis of gene expression microarray data with missing values. Genes are clustered using the Fuzzy C-Means algorithm, followed by approximating the fuzzy partition by a probabilistic data distribution model which is then used to estimate the missing values in the dataset. Using distributionbased approach, our method is most appropriate for datasets where the data are nonuniform. We show that our method outperforms six popular imputation algorithms on uniform and nonuniform artificial datasets as well as real datasets with unknown data distribution model.
 
  Presentation This will be presented at the 9th International Conference on Information Technology: New Generations - ITNG12 (April 16-18, 2012, Las Vegas, Nervada USA), scheduled on April 16th 2012 at 10:30am-10:50am.
  Online demo This online demo was developed using AJAX technology, PHP and C++ programming languages.
 
  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
 
  Gene expression datasets This package contains two gene expression datasets: yeast, yeast-MIPS from Ka Yee Yeung
 
  Additional gene expr. datasets This package contains two gene expression datasets, RCNS (Wen et al., 1998) and Serum (Iyer et al., 1999); the benchmark reports were added to the final version.
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  Artificial dataset results This package contains the performance results of fzDBI and the other imputation methods on two artificial datasets. This is reported in the Figures 2&3 of the manuscript.
 
  Real dataset results This package contains the performance results of fzDBI and the other imputation methods on the two datasets: iris and wine. This is reported in the Figures 4&5 of the manuscript.
 
  Gene expression dataset results This package contains the performance results of fzDBI and the other imputation methods on the two gene expression datasets: yeast andd yeast-MIPS. This is reported in the Figures 7&8 of the manuscript.
 
  Additional gene expr. dataset results This package contains the performance results of fzDBI and the other imputation methods on the two gene expression datasets, RCNS and Serum, reported as in Figures 6&9 of the manuscript.