Biclustering is a data-mining technique that allows simultaneous clustering of rows and columns within a matrix. This book focuses on biclustering as an unsupervised learning method for high-dimensional data and big data structure. The book covers this increasingly important methodology and illustrates its versatility beyond the analysis of gene expression. The book also provides easy-to-follow illustrations of how biclustering can be implemented in practice using the R-based user-friendly graphical interface BiclustGUI.
Adetayo Kasim is affiliated with Durham University, UK.
Ziv Shkedy is affiliated with Hasselt University, Center for Statistics.
Sebastian Ka is affiliated with GmbH & Co, Germany.
Sepp Hochreiter is affiliated with Johannes Kepler University, Linz, Austria.
Willem Talloen is affiliated with Janssen Pharmaceuticals.
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