Written by Suresh K. Gorakala and Michele Usuelli, published by Packt Press 2015
This is whole book on a topic that is often only a single chapter in a book. It is a book for people who already know R and machine learning .
The book uses Math equations not just code for teaching the concepts.
Covers confusion matrix for classification. Along with sensitivity and specification. Lots of details about type one and type two errors. This clearly written section will help you understand why you don’t want either type of error and what they are.
Classification similarity measures include Euclidean Distance, Cosine Distance and Pearson Correlation.
Dimensionality reduction techniques include Principle Component Analysis.
Data Mining techniques include K-means clustering and Support Vector Machine.
Recommender System includes collaborative filtering and content based filtering.
R package for the book is recommenderlab.
recommenderlab: Lab for Developing and Testing Recommender Algorithms by Michael Hahsler at http://CRAN.R-project.org/package=recommenderlab
Other packages used are lsa, e1071, cluster.