In the years since the first edition of this book, data mining has grown to become an indispensable tool of modern business. The book retains the focus of earlier editions showing marketing analysts, business managers, and data mining specialists how to harness data mining methods and techniques to solve important business problems. After establishing the business context with an overview of data mining applications, and introducing aspects of data mining methodology common to all data mining projects, the book covers each important data mining technique in detail. The companion website provides data that can be used to test out the various data mining techniques in the book.
The desire to predict discoveries--to have some idea, in advance, of what will be discovered, by whom, when, and where--pervades nearly all aspects of modern science, from individual scientists to publishers, from funding agencies to hiring committees. In this Essay, we survey the emerging and interdisciplinary field of the "science of science" and what it teaches us about the predictability of scientific discovery. We then discuss future opportunities for improving predictions derived from the science of science and its potential impact, positive and negative, on the scientific community.
A major challenge for using data to make predictions is distinguishing what is meaningful from noise. As this special section explores, prediction is now a developing science. Social scientists and the machine learning community are acquiring new analytical tools to distinguish meaningful patterns from noise. Several authors in this special section describe the importance of realistic goals that seek to balance machine learning approaches with the human element.