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  • 1

    Unsupervised learning in R

    Free

    The k-means algorithm is one common approach to clustering. Learn how the algorithm works under the hood, implement k-means clustering in R, visualize and interpret the results, and select the number of clusters when it's not known ahead of time. By the end of the chapter, you'll have applied k-means clustering to a fun "real-world" dataset!

  • 2

    Hierarchical clustering

    Hierarchical clustering is another popular method for clustering. The goal of this chapter is to go over how it works, how to use it, and how it compares to k-means clustering.

  • 3

    Dimensionality reduction with PCA

    Principal component analysis, or PCA, is a common approach to dimensionality reduction. Learn exactly what PCA does, visualize the results of PCA with biplots and scree plots, and deal with practical issues such as centering and scaling the data before performing PCA.

  • 4

    Putting it all together with a case study

    The goal of this chapter is to guide you through a complete analysis using the unsupervised learning techniques covered in the first three chapters. You'll extend what you've learned by combining PCA as a preprocessing step to clustering using data that consist of measurements of cell nuclei of human breast masses.

Initializing

Exercise

Results of hierarchical clustering

Let's use the hierarchical clustering model you just created to determine a height (or distance between clusters) where a certain number of clusters exists. The variables you created before—wisc.data, diagnosis, wisc.pr, pve, and wisc.hclust—are all available in your workspace.

Using the plot() function, what is the height at which the clustering model has 4 clusters?

Instructions

50 XP

Possible answers