<P>推荐一本北美MBA课程Marketing Informatics中使用的Data Mining 教材。</P>
<P></P>
<P><br> <br><br><br><br><br></P>
<P><br>这是书的封面介绍 </P>
<P><EM><U><STRONG><FONT size=4>Introduction</FONT></STRONG></U></EM><br><br><STRONG>The book is divided into three parts.</STRONG> </P>
<P><U>The first part</U> talks about the business<br>context of data mining, starting with a chapter that introduces data mining<br>and explains what it is used for and why. The second chapter introduces<br>the virtuous cycle of data mining — the ongoing process by which data mining<br>is used to turn data into information that leads to actions, which in turn<br>create more data and more opportunities for learning. Chapter 3 is a muchexpanded<br>discussion of data mining methodology and best practices. This<br>chapter benefits more than any other from our experience since writing the<br>first book. The methodology introduced here is designed to build on the successful<br>engagements we have been involved in. Chapter 4, which has no counterpart<br>in the first edition, is about applications of data mining in marketing<br>and customer relationship management, the fields where most of our own<br>work has been done.<br><U>The second part</U> consists of the technical chapters about the data mining<br>techniques themselves. All of the techniques described in the first edition are<br>still here although they are presented in a different order. The descriptions<br>have been rewritten to make them clearer and more accurate while still retaining<br>nontechnical language wherever possible.<br>In addition to the seven techniques covered in the first edition — decision<br>trees, neural networks, memory-based reasoning, association rules, cluster<br>detection, link analysis, and genetic algorithms — there is now a chapter on<br>data mining using basic statistical techniques and another new chapter on survival<br>analysis. Survival analysis is a technique that has been adapted from the<br>small samples and continuous time measurements of the medical world to the<br>Introduction xxv<br>large samples and discrete time measurements found in marketing data. The<br>chapter on memory-based reasoning now also includes a discussion of collaborative<br>filtering, another technique based on nearest neighbors that has<br>become popular with Web retailers as a way of generating recommendations.<br><U>The third part</U> of the book talks about applying the techniques in a business<br>context, including a chapter on finding customers in data, one on the relationship<br>of data mining and data warehousing, another on the data mining environment<br>(both corporate and technical), and a final chapter on putting data<br>mining to work in an organization. A new chapter in this part covers preparing<br>data for data mining, an extremely important topic since most data miners<br>report that transforming data takes up the majority of time in a typical data<br>mining project.<br>Like the first edition, this book is aimed at current and future data mining<br>practitioners. It is not meant for software developers looking for detailed<br>instructions on how to implement the various data mining algorithms nor for<br>researchers trying to improve upon those algorithms. Ideas are presented in<br>nontechnical language with minimal use of mathematical formulas and arcane<br>jargon. Each data mining technique is shown in a real business context with<br>examples of its use taken from real data mining engagements. In short, we<br>have tried to write the book that we would have liked to read when we began<br>our own data mining careers.<br>— Michael J. A. Berry, October, 2003<br></P>

[此贴子已经被作者于2007-4-6 4:31:55编辑过]