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An overview to the sampling nodes in SAS^® Enterprise Miner v4.3 to read the data and learn about the various methods to both randomly sample and partition the data within the process flow in SAS^® Enterprise Miner v4.3.

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An overview to the explore nodes that are usually used as a preliminary step in data mining in order to observe the distribution or range of values to each variable separately or as a group to discovering relationships or patterns in the data from the assortment of charts and graphs that are generated from the nodes. In addition, market basket analysis can be performed in order to determine the strength of the association between various items of interest.

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An overview to the modify nodes that are designed to perform preliminary modification to the data set in preparation to data mining analysis by removing extreme values or outlandish observations, estimating missing observations in the data, collapsing and massaging the data in preparation to time series modeling, performing clustering analysis in determining various groupings and characteristics within each group that is created to determining the best groupings to form in the data based on a binary-valued variable. 

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An overview to the modeling nodes that will make the readers get familiar with the assortment of modeling designs that can be performed within Enterprise Miner such as traditional regression modeling, logistic regression modeling, decision tree modeling, neural network modeling, principal components modeling, and nearest neighbor modeling. The book will introduce the readers to an additive nonlinear modeling design called dmneural network modeling. The model is designed to predict either an interval or binary target variable in which the predicted values are calculated from three separate models. The reason it is called an additive nonlinear modeling design is because the first model will predict the target variables, with the subsequent models predicting the residual values from the previous model. The fitted values are calculated by adding the separate nonlinear models. In addition, the book will introduce the readers to a unique two-stage modeling design that can be performed within SAS^® Enterprise Miner. The two-stage modeling is designed to fit two separate models in succession by combining the probability estimates from the initial classification model to predict the continuous response variable in the subsequent predictive model. In addition, the book will introduce a very powerful modeling node in Enterprise Miner called ensemble modeling. Ensemble modeling is designed to increase the accuracy, stability and consistency of the modeling estimates by either averaging the prediction estimates from different models or averaging the prediction estimates based on successive fits of the same predictive model. In addition, the Ensemble node has the added capability to perform a couple resampling techniques called bagging and boosting resampling. Bagging is similar to bootstrap sampling in which you may iteratively generate your modeling estimates by resampling the data with replacement, then averaging the fitted values that results in the final modeling estimates. Boosting is a classification modeling technique in which the estimated probabilities are adjusted by weight estimates where the weights are increased when the classification model misclassified the target categories from the previous model.

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An overview to the assessment nodes that will allow you to evaluate and assess the results that are generated from the numerous models within the process flow by viewing the various performance charts that are generated to determine which classification model performs best to determining the accuracy of the predictive models from the various assessment statistics and diagnostic charts that are generated from the node. The book will introduce the readers to the Reporter node that assembles and consolidates the numerous option settings and result listings from the various nodes in the process flow diagram into a single HTML report that can be viewed by your favorite Web browser.

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The remaining sections to the book will explain the rest of the other nodes in Enterprise Miner such as the Score node that will allow you to manage, edit, execute and create your own custom-designed score code in order to generate entirely different estimates from a new set of values in data mining analysis. This will be followed by the SAS Code node that is one of the most powerful nodes within SAS^® Enterprise Miner that will allow you to perform SAS programming within the process flow.