Introduction

In the last couple of decades, we have witnessed a signifi cant increase in the volume of data in our daily life-there is data available for almost all aspects of life. Almost every individual, company and organization has created and can access a large amount of data and information recording the historical activities of themselves when they are interacting with the surrounding world. This kind of data and information helps to provide the analytical sources to reveal the evolution of important objects or trends, which will greatly help the growth and development of business and economy. However, due to the bottleneck of technological advance and application, such potential has yet been fully addressed and exploited in theory as well as in real world applications. Undoubtedly, data mining is a very important and active topic since it was coined in the 1990s, and many algorithmic and theoretical breakthroughs have been achieved as a result of synthesized efforts of multiple domains, such as database, machine learning, statistics, information retrieval and information systems. Recently, there has been an increasing focus shift in data mining from algorithmic innovations to application and marketing driven issues, i.e., due to the increasing demand from industry and business, more and more people pay attention to applied data mining. This book aims at creating a bridge between data mining algorithms and applications, especially the newly emerging topics of applied data mining. In this chapter, we fi rst review the related concepts and techniques involved in data mining research and applications. The layout of this book is then described from three perspectives-fundamentals, advanced data mining and emerging applications. Finally the readership of this book and its purpose is discussed.