SAS Enterprise Miner 5.2 is the SAS data mining solution that addresses the entire data mining process using an intuitive Java point-and-click interface. This guide introduces you to the core functionality of SAS Enterprise Miner and shows you how to perform basic data mining tasks. You will learn how to use the graphical user interface (GUI) tools to create and manage process flow diagrams and projects, and to export mining results for reporting and integration with other SAS software. The data mining tasks you will learn include sampling, exploring, modifying, modeling, and assessing data in order to create and refine predictive models. Getting Started with Enterprise Miner 5.2 provides step-by-step examples that create a complete process flow diagram, including graphic results. This title is also available online. This title is intended for statisticians, quantitative analysts, and business technologists who want to learn to use the data mining capabilities of SAS Enterprise Miner.
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4 of 4 people found the following review helpful:
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Quick introduction,
By Dr. Lee D. Carlson (Baltimore, Maryland USA) - See all my reviews (VINE VOICE) (HALL OF FAME REVIEWER) (REAL NAME)
This review is from: Getting Started With SAS Enterprise Miner 5.2 (Paperback)
For those who need to quickly learn SAS Enterprise Miner 5.2, this book is a good start, as it can be read and its sample projects completed in less than one day. Statisticians and applied mathematicians who have used SAS as a statistical analysis tool throughout the years and now must use it for database programming or data mining may find using SAS in this manner somewhat awkward (not due to the ability of SAS to do these tasks but due to their inexperience), but the Miner 5.2 GUI makes the learning experience much more palatable. The SAS authors have done a fine job of relating essential information on how to use Miner 5.2 without being bogged down in fine technical details. Those readers who intend to use Miner 5.2 for things such as the construction of neural networks have no doubt chosen it because of trust in its algorithms. Coding neural networks by hand is time-consuming, as is the validation of the resulting architecture, so having a tool like Miner 5.2 can assist in quickly obtaining a neural network or for doing rudimentary data mining. Miner 5.2 is flexible, but this reviewer has encountered problems that are very awkward to implement using this software, such as doing predictive time series analysis with feed-forward neural networks assuming an arbitrary lag.
Data mining, which used to be thought of as a subfield of artificial intelligence, has become very popular in many different businesses in the last decade. The goal of data mining is to find patterns in large amounts of data, and various algorithms are used to do this, all of them expressing various biases towards particular notions of what constitutes a cluster or notions of data proximity. The authors expect the reader to be very familiar with data mining, and this expectation allows them to emphasize the Miner 5.2 application rather than spending time on the basic rudiments of data mining. Experts in data mining may find some of the dialog in the book trivial or sophomoric, but if they recognize that the book is designed to teach Miner 5.2 and not data mining, they should find the book helpful.
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diagram workspace, data role, split node, leaf index, exported data, variable summary, profit matrix, metadata server, register models, model nodes, variable selection node, node toolbar, interactive decision tree, score code, node statistics, modeling nodes, scoring code, impute missing values, model package, scored data, process flow diagram, model tab, ellipsis button, complete client Key Phrases - Capitalized Phrases (CAPs): (learn more)
Enterprise Miner, Decision Tree, Input Interval, Statistic Training, Nodes That Model, Cancel Help, Data Sources, File Edit View Window, Neural Network, Transform Variables, Nodes That Modify, Tree Map, Back Next, Getting Started Charitable Giving Example, Select View, Package Reader, Close the Results, Formula Builder, Click Next, Expression Text, Program Editor, Select Chart, Data Partition, Variable Transformation, Configure the Metadata Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
diagram workspace, data role, split node, leaf index, exported data, variable summary, profit matrix, metadata server, register models, model nodes, variable selection node, node toolbar, interactive decision tree, score code, node statistics, modeling nodes, scoring code, impute missing values, model package, scored data, process flow diagram, model tab, ellipsis button, complete client Key Phrases - Capitalized Phrases (CAPs): (learn more)
Enterprise Miner, Decision Tree, Input Interval, Statistic Training, Nodes That Model, Cancel Help, Data Sources, File Edit View Window, Neural Network, Transform Variables, Nodes That Modify, Tree Map, Back Next, Getting Started Charitable Giving Example, Select View, Package Reader, Close the Results, Formula Builder, Click Next, Expression Text, Program Editor, Select Chart, Data Partition, Variable Transformation, Configure the Metadata Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
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