Data Mining Techniques 数据开采技术：营销、销售与客户关系管理 夸克云 txt pdf umd tct 百度云 kindle 下载

Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems.

Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support.

The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining.

More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining.

Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis.

作者简介：

MICHAEL J. A. BERRY and GORDON S. LINOFF are the founders of Data Miners, Inc., a consultancy specializing in data mining. They have jointly authored some of the leading data mining titles in the field, Data Mining Techniques, Mastering Data Mining, and Mining the Web (all from Wiley). They each have more than a decade of experience applying data mining techniques to business problems in marketing and customer relationship management.

Acknowledgments

About the Authors

Introduction

Chapter 1: Why and What Is Data Mining?

Chapter 2: The Virtuous Cycle of Data Mining

Chapter 3: Data Mining Methodology and Best Practices

Chapter 4: Data Mining Applications in Marketing and Customer Relationship Management

Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools

Chapter 6: Decision Trees

Chapter 7: Artificial Neural Networks

Chapter 8: Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering

Chapter 9: Market Basket Analysis and Association Rules

Chapter 10: Link Analysis

Chapter 11: Automatic Cluster Detection

Chapter 12: Knowing When to Worry: Hazard Functions and Survival Analysis in Marketing

Chapter 13: Genetic Algorithms

Chapter 14: Data Mining throughout the Customer Life Cycle

Chapter 15: Data Warehousing, OLAP, and Data Mining

Chapter 16: Building the Data Mining Environment

Chapter 17: Preparing Data for Mining

Chapter 18: Putting Data Mining to Work

Index

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...build a model based on people who had ever responded to any offer in the past. Such a model would be good for discriminating between people who refuse all telemarketing calls and throw out all junk mail, and those who occasionally respond to some offers. These types of models are called nonresponse models...

When missing values must be replaced, the best approach is to impute them by creating a model that has the missing value as its target variable.

书籍介绍

Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis