How This Book is Organized
The content of the book is divided into two parts: chapters 1 – 8, and chapters 9 - 11.
The first eight chapters constitute the bulk of the book, and serve to thoroughly ground the reader in the practice of data mining in the modern enterprise. These chapters focus on the “what”, “when”, “why”, and “how” of data mining practice. Technical complexities are introduced only when they are essential to the treatment. This is the part of the book that everyone should read; later chapters assume that the reader is familiar with the concepts and terms presented in these chapters.
Chapter 1 is a data mining manifesto: it describes the mindset that characterizes the successful data mining practitioner. It delves into some philosophical issues underlying the practice (e.g., “Why is it ESSENTIAL that the data miner understand the difference between ‘data’ and ‘information’?”).
Chapter 2 provides a summary treatment of data mining as a six-step spiral process.
Each of chapters 3 – 8 is devoted to one of the steps of the data mining process. Checklists, case studies, tables, and figures abound.
The last three chapters, 9 – 11, are devoted to specific categories of data mining practice, referred to here as “genres”. The data mining genres addressed are:
Chapter 9 (Supervised Learning) Detecting and Characterizing Known Patterns
Chapter10 (Forensic Analysis) Detecting, Characterizing, and Exploiting Hidden Patterns
Chapter 11 Knowledge: its Acquisition, Representation, and Use
It is hoped that the reader will benefit from this rendition of the author’s extensive experience in data mining/modeling, pattern processing, and automated decision support. He started this journey in 1979, and learned most of this stuff the hard way. By repeating his successes and avoiding his mistakes, you make his struggle worthwhile!