Exercise

# K-means clustering: first exercise

This exercise will familiarize you with the usage of k-means clustering on a dataset. Let us use the Comic Con dataset and check how k-means clustering works on it.

Recall the two steps of k-means clustering:

- Define cluster centers through
`kmeans()`

function. It has two required arguments: observations and number of clusters. - Assign cluster labels through the
`vq()`

function. It has two required arguments: observations and cluster centers.

The data is stored in a Pandas data frame, `comic_con`

. `x_scaled`

and `y_scaled`

are the column names of the standardized X and Y coordinates of people at a given point in time.

Instructions

**100 XP**

- Import
`kmeans`

and`vq`

functions in SciPy. - Generate cluster centers using the
`kmeans()`

function with two clusters. - Create cluster labels using these cluster centers.