April 2017
Beginner to intermediate
420 pages
9h 58m
English
book
Mastering Machine Learning with R - Second Edition
by Cory Lesmeister, Doug Ortiz, Vikram Dhillon, Miroslav Kopecky
Content preview from Mastering Machine Learning with R - Second Edition
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Gower and PAM
To begin this step, we will need to wrangle our data a little bit. As this method can take variables that are factors, we will convert alcohol to either high or low content. It also takes only one line of code utilizing the ifelse() function to change the variable to a factor. What this will accomplish is if alcohol is greater than zero, it will be High, otherwise, it will be Low:
> wine$Alcohol <- as.factor(ifelse(df$Alcohol > 0, "High", "Low"))
We are now ready to create the dissimilarity matrix using the daisy() function from the cluster package and specifying the method as gower:
> disMatrix <- daisy(wine[, -1], metric = "gower")
The creation of the cluster object--let's call it pamFit--is done with the pam() function, ...
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