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How This Book is OrganizedThe 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! Related Links |
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Practical Data Mining 
How to Use This Book 