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Model-Based Clustering and Classification for Data Science - With Applications in R (Hardcover)
Loot Price: R2,026
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Model-Based Clustering and Classification for Data Science - With Applications in R (Hardcover)
Series: Cambridge Series in Statistical and Probabilistic Mathematics
Expected to ship within 12 - 17 working days
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Cluster analysis finds groups in data automatically. Most methods
have been heuristic and leave open such central questions as: how
many clusters are there? Which method should I use? How should I
handle outliers? Classification assigns new observations to groups
given previously classified observations, and also has open
questions about parameter tuning, robustness and uncertainty
assessment. This book frames cluster analysis and classification in
terms of statistical models, thus yielding principled estimation,
testing and prediction methods, and sound answers to the central
questions. It builds the basic ideas in an accessible but rigorous
way, with extensive data examples and R code; describes modern
approaches to high-dimensional data and networks; and explains such
recent advances as Bayesian regularization, non-Gaussian
model-based clustering, cluster merging, variable selection,
semi-supervised and robust classification, clustering of functional
data, text and images, and co-clustering. Written for advanced
undergraduates in data science, as well as researchers and
practitioners, it assumes basic knowledge of multivariate calculus,
linear algebra, probability and statistics.
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