Clustering has become an increasingly important topic in recent years, caused by the glut of data from a wide variety of disciplines. However, due to the lack of good communication among these communities, similar theories or algorithms are redeveloped many times, causing unnecessary waste of time and resources. Furthermore, different terminologies confuse practitioners, especially those new to cluster analysis. Clear and comprehensive information in this fi eld is needed. This need, among others, has encouraged us to produce this book, seeking to provide a comprehensive and systematic description of the important clustering algorithms rooted in statistics, computer science, computational intelligence, and machine learning, with an emphasis on the new advances in recent years. The book consists of 11 chapters, ranging from the basic concept of cluster analysis, proximity measures, and cluster validation, to a wide variety of clustering algorithms, including hierarchical clustering, partitional clustering, neural network - based clustering, kernel - based clustering, sequential data clustering, large - scale data clustering, and high dimensional data clustering. It also includes rich references and illustrates examples in recent applications, such as bioinformatics and web document clustering. Exercises are provided at the end of the chapters to help readers understand the corresponding topics.