Bagging (Breiman 1996) is a common ensemble algorithm, in which you do the following: Develop separate models on k random samples of the data of about the same size.       2. Fit a classification or regression tree to each sample. I tend to bag only trees but the start and end group nodes.  allow other algorithms.       3. Average or vote to derive the final predictions or classifications.  Boosting (Freund and Schapire, 1996) ,also supported through start  >  tree > end , goes one step further and weights observations that are misclassified in the previous models more heavily for inclusion into subsequent samples.  The successive samples are adjusted to accommodate previously computed inaccuracies. Gradient boosting (Friedman 2001) resamples the training data several times to generate results that form a weighted average of the resampled data set.  Each tree in the series is fit to the residual of the prediction from the earlier trees in the series. The residual is defined in terms of the derivative of a loss function. For squared error loss and an interval target, the residual is simply the target value minus the predicted value.  Because each successive sample is weighted according to the classification accuracy of previous models, this approach is sometimes called stochastic gradient boosting. Random forests is my favorite data mining algorithm especially, when I have little subject knowledge of the application.   You grow many large decision trees at random and vote over all trees in the forest. The algorithm works as follows: You develop random samples of the data and grow k decision trees.  The size of k is large, usually greater than or equal to 100.   A typical sample size is about two-thirds of the training data. At each split point for each tree you evaluate a random subset of candidate inputs (predictors) are evaluated. You hold the size of the subset constant across all trees. You grow each tree is as large as possible without pruning. In a random forest this case you are perturbing not only the data but also the variables that are used to construct each tree. The error rate is measured on the remaining holdout data not used for training.   This remaining one-third of the data is called the out-of-bag sample.  Variable importance can also be inferred based on how often an input was used in the construction of the trees.    ... View more

HI Sonnfan,  It appears you want to cluster variables and not observations.  In that case, you can use variable clustering node, or factor analysis (see PROC FACTOR) or princicpal components.   If you want to cluster rows, the for binary data, the Euclidean distance measure used by K-Means is equivalent to counting the number of variables on which two cases disagree. However, you can try one of the following approaches: Run proc distance by a selecting the distance type that you want and apply clustering using that distance matrix. OR You can project the binary variables and do clustering as follows: run Factor Analysis or PCA on the binary variables save the factor or component scores as new variables cluster on the basis of those scores (In that case, the data will no longer be binary) Good luck ... View more

Some users may wish to override default Enterprise Miner interactive decision tree sampling strategies. Enterprise Miner provides two macros that you can issue with your project startup code that will modify interactive decision tree input data sampling behaviors: %let EM_INTERACTIVE_TREE_MAXOBS= <max-number-of-observations-in-sample>; %let EM_INTERACTIVE_TREE_SAMPLEMETHOD=<RANDOM | FIRSTN>; The first macro specifies the maximum number of observations that can exist in an Interactive Decision Tree node sample. You use this macro if you want to manually control the sample size. Otherwise, Enterprise Miner will use its own algorithms to perform sampling for your interactive decision tree. The second macro specifies the sampling methodology that will be used to create an Interactive Decision Tree node sample. You can use this macro if you want to manually control the methodology Enterprise Miner uses to create interactive decision tree samples. By default, Enterprise Miner uses random sampling for interactive decision trees. You can use the macro to choose between RANDOM and FIRSTN sample creation. You use the EM_INTERACTIVE_TREE_MAXOBS macro to specify the number of observations for both RANDOM and FIRSTN sampling strategies. ... View more

Please also see this tech support note http://support.sas.com/kb/38/578.html As you indicate I would use the SAS Code node to rename the variables using the macros varaible names for the data sources you want to import and export. SAS has a 32 character limit. Teradata has a 30 character limit. Long variable names is something we look into. ... View more

Thanks for using SAS EM. Please see this usage note about max levels exceed 512. http://support.sas.com/kb/20/054.html Also as suggested I would recommend being careful about using a variable like zip code or other nominal variables that have a really large number of discrete nominal values as predictors (inputs). You can reject this variable or using a binning technique. ... View more