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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

k-means clustering and comparing results

As you now know, there are two main types of clustering: hierarchical and k-means.

In this exercise, you will create a k-means clustering model on the Wisconsin breast cancer data and compare the results to the actual diagnoses and the results of your hierarchical clustering model. Take some time to see how each clustering model performs in terms of separating the two diagnoses and how the clustering models compare to each other.

Instructions

100 XP

wisc.data, diagnosis, and wisc.hclust.clusters are still available.

  • Create a k-means model on wisc.data, assigning the result to wisc.km. Be sure to create 2 clusters, corresponding to the actual number of diagnosis. Also, remember to scale the data and repeat the algorithm 20 times to find a well performing model.
  • Use the table() function to compare the cluster membership of the k-means model to the actual diagnoses contained in the diagnosis vector. How well does k-means separate the two diagnoses?
  • Use the table() function to compare the cluster membership of the k-means model to the hierarchical clustering model. Recall the cluster membership of the hierarchical clustering model is contained in wisc.hclust.clusters.