Preface
About
This section briefly introduces the author, the coverage of this book, the technical skills you'll need to get started, and the hardware and software requirements required to complete all of the included activities and exercises.
About the Book
Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions.
This book begins with the most important and commonly used method for unsupervised learning – clustering – and explains the three main clustering algorithms: k-means, divisive, and agglomerative. Following this, you'll study market ...
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