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LAB 4H: Finding Clusters

Lab 4H - Finding clusters

Directions: Follow along with the slides, completing the questions in blue on your computer, and answering the questions in red in your journal.

Clustering data

  • We've seen previously that data scientists have methods to predict values of specific variables.

    – We used regression to predict numerical values and classification to predict categories.

  • Clustering is similar to classification in that we want to group people into categories. But there's one important difference:

    – In clustering, we don't know how many groups to use because we're not predicting the value of a known variable!

  • In this lab, we'll learn how to use the k-means clustering algorithm to group our data into clusters.

The k-means algorithm

  • The k-means algorithm works by splitting our data into k different clusters.

    – The number of clusters, the value of k, is chosen by the data scientist.

  • The algorithm works only for numerical variables and only when we have no missing data.

  • To start, use the data function to load the futbol data set.

    – This data contains 23 players from the US Men's National Soccer team (USMNT) and 22 quarterbacks from the National Football League (NFL).

  • Create a scatterplot of the players ht_inches and wt_lbs and color each dot based on the league they play for.

Running k-means

  • After plotting the player's heights and weights, we can see that there are two clusters, or different types, of players:

    – Players in the NFL tend to be taller and weigh more than the shorter and lighter USMNT players.

  • Fill in the blanks below to use k-means to cluster the same height and weight data into two groups:

    kclusters(____~____, data = futbol, k = ____)

  • Use this code and the mutate function to add the values from kclusters to the futbol data. Call the variable clusters.

k-means vs. ground-truth

  • In comparing our football and soccer players, we know for certain which league each player plays in.

    – We call this knowledge ground-truth.

  • Knowing the ground-truth for this example is helpful to illustrate how k-means works, but in reality, data-scientists would run k-means not knowing the ground-truth.

  • Compare the clusters chosen by k-means to the ground-truth. How successful was k-means at recovering the league information?

On your own

  • Load your class' timeuse data (remember to run timeuse_format so each row represents the mean time each student spent participating in the various activities).

  • Create a scatterplot of homework and videogames variables.

    – Based on this graph, identify and remove any outliers by using the filter function.

  • Use kclusters with k=2 for homework and videogames.

    – Describe how the groups differ from each other in terms of how long each group spends playing videogames and doing homework.