DATA MINING USING SAS ENTERPRISE MINER RANDALL MATIGNON PDF

Link Analysis Node On page under the Transactions tab, I would like to add additional comments highlighted in bold to the Minimum count and Retain path position options under the Sequence section of the Link Analysis node. The Sequence section will be available for selection assuming that you have sequence data, that is, a sequence or a time stamp variable, in the active training data set. The Sequence section has the following options for configuring the sequence: Minimum count: Specifies the minimum number of items that occur, defined as a sequence to the analysis. By default, a sequence is defined by two separate occurrences. For instance, analyzing people visiting various Web pages, setting this option to one will ensure you that you will capture all people visiting various Web pages, even those customers who visit a single Web site.

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Enterprise Miner Enterprise Miner v. It consists of a variety of analytical tools like neural networks to support data mining to enhance traditional forecasting modeling. Data mining is an analytical tool that is used in solving critical business decisions by analyzing enormous amounts of data in order to discover relationships and unknown patterns in the data. Enterprise Miner is a powerful product now available within the SAS software.

The EM data mining SEMMA methodology is specifically designed to handling enormous data sets in preparation to subsequent data analysis. Neural network modeling with regard to the data mining tasks falls under predictive modeling i. In regression modeling, the aim is building a model that will produce values of one variable to be predicted based on a set of known values of other variables. In classification modeling, the difference is that the variable to predict is categorical based on a set of known quantitative variables.

Purpose of Writing this Book One reason for writing this book is because there is not a tremendous amount of written literature on neural network modeling using SAS Enterprise Miner. This book consists of a step-by-step approach in designing a neural network process flow diagram using SAS Enterprise Miner.

There are numerous examples in explaining the various complex neural network designs and optimization techniques used in network modeling with numerous examples taken from various SAS literature comparing the forecasting results between both neural network and traditional regression forecasting techniques with an explanation to the SAS modeling results.

Highlights to this Book Chapter 2 discusses basic model building and the various modeling assumptions that need to be satisfied. These modeling assumptions in order of importance are independence, equal variance, and normality in the modeling terms must be satisfied in both traditional regression modeling and neural network designs.

However, it should be noted that some neural network modelers ignore these same important modeling assumptions. This section will explain the various diagnosis statistic used in identifying outliers and influential data points that have a profound effect to the modeling results. And finally explaining the various goodness-of-fit statistics used in determining the best linear combination of input variables among a pool of all possible combination of input variables to the regression model.

Chapter 3 explains the neural network design and the various configuration settings. The section will first explain a simple perceptron design for a binary-valued target variable.

Next, we will discuss the neural network designs and the various configurations to the design like the various layers, weights, combination functions, transfer functions, objective or error functions, and optimization techniques that are used.

The section will explain the various optimization techniques such as the various line search and grid search techniques. It will be followed by various numerical examples in order to simplify the complexity of the numerous optimization techniques that are applied in calculating the neural network weight estimates and determining the smallest error to the neural network model.

A numerical example of the backpropagation algorithm will be presented that is typically used in a neural network MLP design. The section will explain the similarity between the multiple regression parameter estimates and the neural network weight estimates. Pruning techniques used in pre-processing the model will be discussed leading to the general strategies in interpreting important input variables to the neural network model and constructing a well-designed neural network model.

The section will conclude with a brief summary to the advantages and disadvantages of a neural network design. The chapter will then display diagrams of the neural network architecture in a couple of modeling comparison examples presented later in the book. Thereby, for the reader to graphically understand the neural network configuration between the various layers and the weight estimates associated with these same neural network layers.

DATA MINING USING SAS ENTERPRISE MINER RANDALL MATIGNON PDF

Data Mining Using SAS Enterprise Miner

One reason why is because the first principal component is first entered into the model, then followed by the second principal component variable. In other words, the nearest neighbor modeling estimates are calculated similar to moving average estimates in which the first k-values are averaged by the sorted values of the first variable in the model within the subsequent values of the second variable. In Enterprise Miner, the probe x is defined by the sorted values of the input variables that are created in the SAS data set. Since it is recommended in using the principal component scores with numerous input variables to the analysis, then the probe x is determined by the sorted values of the principal component scores.

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Data Mining Using SAS Enterprise Miner Blog

Until now, there has been no single, authoritative book that explores every node relationship and pattern that is a part of the Enterprise Miner software with regard to SEMMA design and data mining analysis. Data Mining Using SAS Enterprise Miner introduces readers to a wide variety of data mining techniques and explains the purpose of-and reasoning behind-every node that is a part of the Enterprise Miner software. Each chapter begins with a short introduction to the assortment of statistics that is generated from the various nodes in SAS Enterprise Miner v4. Data Mining Using SAS Enterprise Miner is suitable as a supplemental text for advanced undergraduate and graduate students of statistics and computer science and is also an invaluable, all-encompassing guide to data mining for novice statisticians and experts alike. He has over twenty years of experience as a statistical programmer and applications developer in the pharmaceutical, healthcare, and biotechnology industries, and he has a broad knowledge of several programming languages, including SAS, S-Plus, and PL-SQL. Read more.

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