In this chapter, we started to explore unsupervised learning techniques. We focused on cluster analysis to both provide data reduction and data understanding of the observations. Three methods were introduced: the traditional hierarchical and k-means clustering algorithms along with the Gower metric and PAM for mixed data. We applied these three methods to find a structure in Italian wines coming from three different cultivars and examined the results. In the next chapter, we will continue exploring unsupervised learning, but instead of finding structure among the observations, we will focus on finding structure among the variables in order to create new features that can be used in a supervised learning problem.
Mastering Machine Learning with R
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Mastering Machine Learning with R
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Overview of this book
Table of Contents (20 chapters)
Mastering Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
A Process for Success
Linear Regression – The Blocking and Tackling of Machine Learning
Logistic Regression and Discriminant Analysis
Advanced Feature Selection in Linear Models
More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
Classification and Regression Trees
Neural Networks
Cluster Analysis
Principal Components Analysis
Market Basket Analysis and Recommendation Engines
Time Series and Causality
Text Mining
R Fundamentals
Index
Customer Reviews