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.
- 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.
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 22.214.171.124 Too Many Comparisons? 2.3 Classical Types of Statistical Prediction 2.3.1 Predicting True-or-False: Classification 126.96.36.199 Error Rates 2.3.2 Forecasting Numbers: Regression 188.8.131.52 Distance Measures 2.4 Measuring Predictive Performance 2.4.1 Independent Testing 184.108.40.206 Random Training and Testing 220.127.116.11 How Accurate Is the Error Estimate? 18.104.22.168 Comparing Results for Error Measures 22.214.171.124 Ideal or Real-World Sampling? 126.96.36.199 Training and Testing from Different Time Periods 2.5 Too Much Searching and Testing? 2.6 Why Are Errors Made?<BR
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- © Morgan Kaufmann 1997
- 1st August 1997
- Morgan Kaufmann
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"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