Data Mining For Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel® with XLMiner®, Second Edition

Preface to the Second Edition

Since the book's appearance in early 2007, it has been used in many classes, ranging from dedicated data mining classes to more general business intelligence courses. Following feedback from instructors teaching both MBA and undergraduate courses, as well as students, we revised some of the existing chapters as well as covered two new topics that are central in data mining: data visualization and time series forecasting.

We have added a set of three chapters on time series forecasting (Chapters 15–17), which present the most commonly used forecasting tools in the business world. They include a set of new datasets and exercises, and a new case (in Chapter 18).

The chapter on data visualization provides comprehensive coverage of basic and advanced visualization techniques that support the exploratory step of data mining. We also provide a discussion of interactive visualization principles and tools, and the chapter exercises include assignments to familiarize readers with interactive visualization in practice.

In the new edition we have created separate chapters for the k-nearest-neighbor and naive Bayes methods. The explanation of the naive Bayes classifier is now clearer, and additional exercises have been added to both chapters.

Another addition are brief chapter summaries at the beginning of each chapter.

We have also reorganized the order of some chapters, following readers' feedback. The chapters are now grouped into seven parts: Preliminaries, Data ...