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Big data has become a standard in many areas of application in the last few years and introduces challenges related to methodology and software development. In this book we address these aspects for tasks where local patterns in a large data matrix are of primary interest. A recently very successful way to analyse such data is to apply biclustering methods which aim to find local patterns in a big data matrix.
The book provides an overview for data analysis using biclustering methods from a practical point of view. A part of the technical details of the methods used for the analysis, in particular for biclustering, are not always fully presented. Readers who wish to investigate the full theoretical background of methods discussed in the book are directed to a reference in the relevant chapter of the book. We review and illustrate the use of several biclustering methods presenting case studies in drug discovery, genetics, marketing research, biology, toxicity and sport.
The work presented in this book is a joint effort of many people from various research groups. We would like to thank all our collaborators. Without their work this book could never be published: Dhammika Amaratunga, Mengsteab Aregay, Luc Bijnens, Andreas Bender, Ulrich Bodenhofer, Aakash Chavan Ravindranath, Djork-Arn´e Clevert, Javier Cabrera, Ewoud De Troyer, Sara Dolnicar, Hinrich W. H. G¨ohlmann, Karin Schwarzbauer, G¨unter Klambauer, Tatsiana Khamiakova, Katie Lazarevski, Friedrich Leisch, Dan Lin, Andreas Mayr, Martin Otava, Setia Pramana, Nolen Joy Perualila, Gundula Povysil, Rudradev Sengupta, Bie Verbist, Suzy Van Sanden, Heather Turner, Oswaldo Trelles, Oscar Torreno Tirado, Thomas Unterthiner and Tobias Verbeke. Special thanks to Jennifer Cook (Wolfson Research Institute, Durham University, UK) for helping with the revision of the chapters.
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