You do not need to be an expert in data mining to use this guide or to use MineSet, but understanding your data and what it represents can help you more easily interpret the results. If you have experience with data mining techniques, this guide will still help you learn how the MineSet algorithms work and how they can be applied and visualized.

If you are using Tool Manager to import data from a database into the MineSet tools, you should refer primarily to the MineSet Enterprise Edition Interface Guide to perform the associated tasks. Once the data has been loaded into the various visualization tools, you do not need a database or programming background.

This guide deals with the tasks involved with running MineSet. Most of the book is oriented towards telling you how to run the various tools. A chapter-by-chapter summary can be found in “Structure of This Document”.

For technical details and more complete explanations, refer to the MineSet Enterprise Edition Reference Guide . For very technical information about specific MineSet algorithms, see the list of white papers at http://www.sgi.com/software/mineset/ http:www.sgi.com/software/mineset/mineset_data.html

For information on exporting MineSet to other locations or other applications, working on the command-line, or anything to do with system administration, turn to the MineSet Enterprise Edition Interface Guide .

MineSet also provides a means for third-party products such as AcPro to plug in to the application.

The first three chapters of this guide introduce data mining and the MineSet product. Subsequent chapters concentrate on specific tools and processes as shown:

Chapter 1, “Overview of Data Mining and MineSet Tools”
This chapter provides a brief overview of the principles of data mining, explains terminology, and introduces the processes involved in analytical and visual data mining with the MineSet tool suite.

Chapter 2, “Accessing Data with MineSet”
This chapter describes how to start MineSet and how to use some basic tools to look at data.

Chapter 3, “Shaping the Data”
This chapter describes both the need for, and the process of, transforming original data using the Tool Manager. The Column Importance and Clustering tools for exploring unseen characteristics of the data are explained.

Chapter 4, “Examining Data with the Scatter and Splat Visualizers”
This chapter provides a description of the Scatter and Splat Visualizer interfaces. These tools are valuable for visualizing multidimensional data, statically or by animation.

Chapter 5, “Examining Data with the Tree Visualizer”
This chapter provides a description of the Tree Visualizer tool interface. This tool is valuable for visualizing hierarchical data.

Chapter 6, “Examining Data with the Map Visualizer”
This chapter describes the Map Visualizer tool interface. This tool is useful for data with a geographical or spatial context.

Chapter 7, “Understanding Predictive Modeling”
This chapter describes predictive modeling, as opposed to descriptive modeling, and the varieties of classifiers offered by the MineSet tools.

Chapter 8, “Modeling and Predicting with Decision, Option, and Regression Trees”
This chapter describes how to generate and use the Decision, Option, and Regression tools. These tools are valuable for classifying data according to a set of attributes by making a series of decisions based on those attributes. Option trees can show the influence of splitting on multiple attributes simultaneously. Regression Trees are useful for predicting attributes based on continuous values, such as occur in real life.

Chapter 9, “Modeling and Predicting with the Decision Table Classifier and Visualizer”
This chapter provides a complete description of the Decision Table interface. This tool is valuable for visualizing decisions made in classifying data and for creating a classifier.

Chapter 10, “Modeling and Predicting with the Evidence Classifier and Visualizer”
This chapter describes how to generate and use the Evidence Classifier. This tool is valuable for classifying data by examining the probabilities of a specified result occurring based on a given attribute.

Chapter 11, “Refining Predictive Modeling”
This chapter describes the purpose and use of the Confusion Matrix, Loss Matrix, and ROI and Lift Curves to relate predictive modeling to real life situations.

Chapter 12, “Segmenting the Data with Clustering,”
This chapter provides a description of the Cluster Visualizer interface. This tool is valuable for examining your data and determining clustering patterns.

Chapter 13, “Analyzing Data with Association Rules”
This chapter provides a description of the Association Rules Visualizer. This tool is valuable for mining large datasets, and discovering correlations in that data.

Glossary, “MineSet User's Glossary”
The glossary explains technical words and phrases used frequently in this guide.

The hard copy of this guide shows screen shots and illustrations in black and white. The online version provides these visuals in full color. If you find a particular graphic or screen shot difficult to see in hard copy, refer to that page online for greater clarity.

For general information on data mining and related techniques, you may wish to consult the following books:

Appendix A in the MineSet Enterprise Edition Interface Guide lists more reading if you are interested in more technical information about data mining.

The following type conventions and symbols are used in this guide:

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