Handbook of Cluster Analysis

The application of cluster analysis (collecting entities that belong together in some sense in a class) is widespread in engineering, medicine, biology, social science, and many other areas. This handbook provides a comprehensive overview of cluster analysis methodology and applications. It summarizes all the recent research in one volume. The book helps researchers understand the state of the art in the field, how to apply cluster analysis using existing methods, and how to choose a method and evaluate the result.

Christian Hennig is a senior lecturer in the Department of Statistical Science at University College London. Dr. Hennig is currently secretary of the International Federation of Classification Societies and associate editor ofStatistics and Computing, Computational Statistics and Data Analysis, Advances in Data Analysis and Classification, andStatistical Methods and Applications. His main research interests are cluster analysis, philosophy of statistics, robust statistics, multivariate analysis, data visualization, and model selection.

Marina Meila is a professor of statistics at the University of Washington. She earned a PhD in computer science and electrical engineering from the Massachusetts Institute of Technology. Her long-term interest is in machine learning and reasoning in uncertainty and how these can be performed efficiently on large, complex data sets.

Fionn Murtagh is a professor of data science at University of Derby and Goldsmiths University of London. Dr. Murtagh is a fellow of the International Association for Pattern Recognition, a fellow of the British Computer Society, an elected member of the Royal Irish Academy and Academia Europaea, a member of the editorial boards of many journals, and editor-in-chief of theComputer Journal. His research interests encompass data science and big data analytics.

Roberto Rocci is a professor of statistics in the Department of Economics and Finance at the University of Rome Tor Vergata. Dr. Rocci is associate editor of theStatistical Methods and Applications Journal and board member of the SIS-CLassification and Data Analysis Group (SIS-CLADAG). His research interests include cluster analysis, mixture models, and latent variable models.