IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork.

IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data. The authors of this book are among the very best of these exponents, gurus who, in their brilliant and imaginative use of the tool, have pushed back the boundaries of applied analytics. By reading this book, you are learning from practitioners who have helped define the state of the art.

Follow the industry standard data mining process, gaining new skills at each stage, from loading data to integrating results into everyday business practices. Get a handle on the most efficient ways of extracting data from your own sources, preparing it for exploration and modeling. Master the best methods for building models that will perform well in the workplace.

Go beyond the basics and get the full power of your data mining workbench with this practical guide.

Approach

This is a practical cookbook with intermediate-advanced recipes for SPSS Modeler data analysts. It is loaded with step-by-step examples explaining the process followed by the experts.

Who this book is for

If you have had some hands-on experience with IBM SPSS Modeler and now want to go deeper and take more control over your data mining process, this is the guide for you. It is ideal for practitioners who want to break into advanced analytics.

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Yes, you can access IBM SPSS Modeler Cookbook by Keith McCormick, Dean Abbott, Meta S. Brown, Tom Khabaza, Scott R. Mutchler in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Mining. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Packt Publishing

Year

2013

ISBN

9781849685467

Topic

Computer Science

Subtopic

Data Mining

Index

Computer Science

IBM SPSS Modeler Cookbook

Credits

Foreword

About the Authors

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why Subscribe?

Free Access for Packt account holders

Instant Updates on New Packt Books

Preface

What is CRISP-DM?

Data mining is a business process

The IBM SPSS Modeler workbench

A brief history of the Clementine workbench

Historical introduction to scripting

What this book covers

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Data Understanding

Introduction

Using an empty aggregate to evaluate sample size

Getting ready

How to do it...

How it works...

There's more...

A modified version

See also

Evaluating the need to sample from the initial data

Getting ready

How to do it...

How it works...

There's more...

See also

Using CHAID stumps when interviewing an SME

Getting ready

How to do it...

How it works...

See also

Using a single cluster K-means as an alternative to anomaly detection

Getting ready

How to do it...

How it works...

There's more...

Using an @NULL multiple Derive to explore missing data

Getting ready

How to do it...

How it works...

See also

Creating an Outlier report to give to SMEs

Getting ready

How to do it...

How it works...

See also

Detecting potential model instability early using the Partition node and Feature Selection node

Getting ready

How to do it...

How it works...

See also

2. Data Preparation – Select

Introduction

Using the Feature Selection node creatively to remove or decapitate perfect predictors

Getting ready

How to do it...

How it works...

There's more...

See also

Running a Statistics node on anti-join to evaluate the potential missing data

Getting ready

How to do it...

How it works...

See also

Evaluating the use of sampling for speed

Getting ready

How to do it...

How it works...

There's more...

See also

Removing redundant variables using correlation matrices

Getting ready

How to do it...

How it works...

There's more...

See also

Selecting variables using the CHAID Modeling node

Getting ready

How to do it...

How it works...

There's more...

See also

Selecting variables using the Means node

Getting ready

How to do it...

How it works...

There's more...

See also

Selecting variables using single-antecedent Association Rules

Getting ready

How to do it...

How it works...

There's more...

See also

3. Data Preparation – Clean

Introduction

Binning scale variables to address missing data

Getting ready

How to do it...

How it works...

See also

Using a full data model/partial data model approach to address missing data

Getting ready

How to do it...

How it works...

There's more...

See also

Imputing in-stream mean or median

Getting ready

How to do it...

How it works...

There's more...

See also

Imputing missing values randomly from uniform or normal distributions

Getting ready

How to do it...

How it works...

There's more...

See also

Using random imputation to match a variable's distribution

Getting ready

How to do it...

How it works...

There's more...

See also

Searching for similar records using a Neural Network for inexact matching

Getting ready

How to do it...

How it works...

There's more...

See also

Using neuro-fuzzy searching to find similar names

Getting ready

How to do it...

How it works...

There's more...

See also

Producing longer Soundex codes

Getting ready

How to do it...

How it works...

There's more...

See also

4. Data Preparation – Construct

Introduction

Building transformations with multiple Derive nodes

Getting ready

How to do it...

How it works...

There's more...

Calculating and comparing conversion rates

Getting ready

How to do it...

How it works...

There's more...

See also

Grouping categorical values

Getting ready

How to do it...

How it works...

There's more...

Transforming high skew and kurtosis variables with a multiple Derive node

Getting ready

How to do it...

How it works...

There's more...

Creating flag variables for aggregation

Getting ready

How to do it...

How it works...

There's more...

Using Association Rules for interaction detection/feature creation

Getting ready

How to do it...

How it works...

There's more...

Creating time-aligned cohorts

Getting ready

How to do it...

How it works...

There's more...

5. Data Preparation – Integrate and Format

Introduction

Speeding up merge with caching and optimization settings

Getting ready

How to do it...

How it works...

See also

Merging a lookup table

Getting ready

How to do it...

How it works...

See also

Shuffle-down (nonstandard aggregation)

Getting ready

How to do it...

How it works...

There's more...

See also

Cartesian product merge using key-less merge by key

Getting ready

How to do it...

How it works...

There's more...

See also

Multiplying out using Cartesian product merge, user source, and derive dummy

Getting ready

How to do it...

How it works...

There's more...

See also

Changing large numbers of variable names without scripting

Getting ready

How to do it...

How it works...

There's more...

See also

Parsing nonstandard dates

Getting ready

How to do it...

How it works...

There's more...

Nesting functions into one Derive node

Performing clean downstream of a calculation using a Filter node

Using parameter...