The potential business advantages of data mining are well documented in publications for executives and managers. However, developers implementing major data-mining systems need concrete information about the underlying technical principles—and their practical manifestations—in order to either integrate commercially available tools or write data-mining programs from scratch. This book is the first technical guide to provide a complete, generalized roadmap for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses. Note: If you already own Predictive Data Mining: A Practical Guide, please see ISBN 1-55860-477-4 to order the accompanying software. To order the book/software package, please see ISBN 1-55860-478-2.

Key Features

+ Focuses on the preparation and organization of data and the development of an overall strategy for data mining. + Reviews sophisticated prediction methods that search for patterns in big data. + Describes how to accurately estimate future performance of proposed solutions. + Illustrates the data-mining process and its potential pitfalls through real-life case studies.

Table of Contents

Preface 1 What is Data Mining? 1.1 Big Data 1.1.1 The Data Warehouse 1.1.2 Timelines 1.2 Types of Data-Mining Problems 1.3 The Pedigree of Data Mining 1.3.1 Databases 1.3.2 Statistics 1.3.3 Machine Learning 1.4 Is Big Better? 1.4.1 Strong Statistical Evaluation 1.4.2 More Intensive Search 1.4.3 More Controlled Experiments 1.4.4 Is Big Necessary 1.5 The Tasks of Predictive Data Mining 1.5.1 Data Preparation 1.5.2 Data Reduction 1.5.3 Data Modeling and Prediction 1.5.4 Case and Solution Analyses 1.6 Data Mining: Art or Science 1.7 An Overview of the Book 1.8 Bibliographic and Historical Remarks 2 Statistical Evaluation for Big Data 2.1 The Idealized Model 2.1.1 Classical Statistical Comparison and Evaluation 2.2 It's Big but Is It Biased 2.2.1 Objective Versus Survey Data 2.2.2 Significance and Predictive Value Too Many Comparisons? 2.3 Classical Types of Statistical Prediction 2.3.1 Predicting True-or-False: Classification Error Rates 2.3.2 Forecasting Numbers: Regression Distance Measures 2.4 Measuring Predictive Performance 2.4.1 Independent Testing Random Training and Testing How Accurate Is the Error Estimate? Comparing Results for Error Measures Ideal or Real-World Sampling? Training and Testing from Different Time Periods 2.5 Too Much Searching and Testing? 2.6 Why Are Errors Made?


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© 1997
Morgan Kaufmann
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About the editors

Sholom Weiss

Sholom M. Weiss is a professor of computer science at Rutgers University and the author of dozens of research papers on data mining and knowledge-based systems. He is a fellow of the American Association for Artificial Intelligence, serves on numerous editorial boards of scientific journals, and has consulted widely on the commercial application of advanced data mining techniques. He is the author, with Casimir Kulikowski, of Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems, which is also available from Morgan Kaufmann Publishers.

Nitin Indurkhya

Nitin Indurkhya is on the faculty at the Basser Department of Computer Science, University of Sydney, Australia. He has published extensively on Data Mining and Machine Learning and has considerable experience with industrial data-mining applications in Australia, Japan and the USA.


"I enjoy reading PREDICTIVE DATA MINING. It presents an excellent perspective on the theory and practice of data mining. It can help educate statisticians to build alliances between statisticians and data miners." --Emanuel Parzen, Distinguished Professor of Statistics, Texas A&M University