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CS 506 Spring 2020 - HW1

Clustering and Visualization

Due: October 19, 2020

1 Understanding K-means Clustering

In this exercise, you will implement the K-means clustering algorithm. You will

start on an example 2D dataset that will help you gain an intuition of how the

K-means algorithm works. You will be using k means clustering.py for this part

of the exercise.

1.1 Implementing K-means

The K-means algorithm is a method to automatically cluster similar data examples

together. Concretely, you are given a training set , and want to group the data into a few cohesive clusters. The intuition

behind K -means is an iterative procedure that starts by guessing the initial

centroids, and then refines this guess by repeatedly assigning examples to their

closest centroids and then recomputing the centroids based on the assignments.

The inner loop of the algorithm repeatedly carries out two steps:

• Assigning each training example x to its closest centroid

• Recomputing the mean of each centroid using the points assigned to it.

The K-means algorithm will always converge to some final set of means for

the centroids. Note that the converged solution may not always be ideal and

depends on the initial setting of the centroids. Therefore, in practice the Kmeans

algorithm is usually run a few times with different random initializations.

One way to choose between these different solutions from different random initializations

is to choose the one with the lowest cost function value (distortion).

You will implement the two phases of the K-means algorithm separately in the

next sections.

1.1.1 Finding Closest Centroids

In the cluster assignment phase of the K-means algorithm, the algorithm assigns

every training example x

i

to its closest centroid, given the current positions of

centroids.

where c(i) is the index of the centroid that is closest to x

i

, and j is the position

(index) of the j-th centroid.

Your task is to complete the code in function find closest centroids. This

function takes the data matrix samples and the locations of all centroids inside

centroids and should output a one-dimensional array of clusters that holds the

index (a value in {1, · · · , K}, where K is total number of centroids) of the closest

centroid to every training example. You can implement this using a loop over

every training example and every centroid. Once you have completed the code

in find closest centroids, you can run it and you should see the output [0, 2, 1, ]

corresponding to the centroid assignments for the first 3 examples.

Please take a look at Figure 1 to gain an understanding of the distribution

of the data. It is two dimentional, with x1 and x2.

Figure 1: Result 1

1.1.2 Computing Centroid Means

Given assignments of every point to a centroid, the second phase of the algorithm

recomputes, for each centroid, the mean of the points that were assigned to it.

Specifically, for every centroid k we set

µk :=

where Ck is the set of examples that are assigned to centroid k. Concretely, if

two examples say x

3 and x

5 are assigned to centroid k = 2, then you should

update

µ2 =

1

2

(x(3) + x(5))

2

Once you have completed the code in get centroids, the k means clustering.py

will run your code and output the centroids after the first step of K-means.

The final centroids should be [[1.95399466 5.02557006] [3.04367119 1.01541041]

[6.03366736 3.00052511]].

When you run the next step, the K-means code will produce a visualization

that steps you through the progress of the algorithm at each iteration. Close

figure multiple times to see how each step of the K-means algorithm changes

the centroids and cluster assignments. At the end, your figure should look as

the one displayed in Figure1

1.2 Random Initialization

The initial assignments of centroids for the example dataset in previous section

were designed so that you will see the same figure as Figure1. In practice, a

good strategy for initializing the centroids is to select random examples from

the training set. In this part of the exercise, you should complete the function

choose random centroids. You should randomly permute the indices of the examples

(using random seed 7). Then, it selects the first K examples based on

the random permutation of the indices. This should allow the examples to be

selected at random without the risk of selecting the same example twice. You

will see how random initialization will affect the first few iterations of clustering,

and also possibly, result in a different cluster assignment.

2 Working with the Algorithms

• In this assignment we will be working with the AirBnB dataset, that you

can also find here. Our goal is to visualize areas of the NYC with respect

to the price of the AirBnb listings in those areas.

From the detailed nyc listings.csv file, you will use longitude and latitude

to cluster closeness and price to cluster for expensiveness.

Note that spatial coordinates and price are in different units, so you may

need to consider scaling in order to avoid arbitrary skewed results.

a) [8 pts.] Find clusters using the 3 different techniques we discussed

in class: k-means++, hierarchical, and GMM. Explain your data

representation and how you determined certain parameters (for example,

the number of clusters in k-means++).

b) [3 pts.] List a few bullet points describing the pros and cons of the various

clustering algorithms.

A few hints:

-Some listings contain missing values. Better strategy for this assignment is to

completely ignore those listings.

3

-Pay attention to the data type of every column when you read a .csv file and

convert them to the appropriate types (e.g. float or integer).

3 Data visualization

a) [1pt.] Start by producing a Heatmap using the Folium package (you can

install it using pip). You can use the code below to help you (assumes the

use of Pandas Dataframes):

def generateBaseMap ( d e f a u l t l o c a t i o n =[ 4 0. 6 9 3 9 4 3 ,

−7 3. 9 8 5 8 8 0] ) :

base map = f oli um .Map( l o c a t i o n=d e f a u l t l o c a t i o n )

return base map

base map = generateBaseMap ( )

HeatMap ( data=d f [ [ ’ l a t i t u d e ’ , ’ l o n gi t u d e ’ , ’ p r i c e ’ ] ] .

groupby ( [ ’ l a t i t u d e ’ , ’ l o n gi t u d e ’ ] ) . mean ( ) .

r e s e t i n d e x ( ) . v al u e s . t o l i s t ( ) , r a di u s =8, max zoom

=13) . add t o ( base map )

base map . s a ve ( ’ inde x . html ’ )

Is this heatmap useful in order to draw conclusions about the expressiveness

of areas within NYC? If not, why?

b) [2pts.] Visualize the clusters by plotting the longitude/latitude of every

listing in a scatter plot.

c) [2pts.] For every cluster report the average price of the listings within this

cluster.

d) Bonus points [1pt.] if you provide a plot on an actual NYC map! You

may use Folium or any other package for this.

e) [1pt.] Are the findings in agreement with what you have in mind about

the cost of living for neighborhoods in NYC? If you are unfamiliar with

NYC, you can consult the web.

4 Image Manipulation

a) [8 pts.] Download the image found by clicking here. For this assignment,

you will use the k-means algorithm in the CS506 python package that you

built in class to manipulate this image. The goal is to give this image as

input, and return the image with like pixels combined into 10 clusters.

A few hints:

-There are a number of useful packages for working with images; we recommend

using cv2 (obtained by running pip install opencv). Using this

package, you can use the line img = cv2.imread(“file.jpg”) in order to

load the image as a numpy array (note that this means you will also need

to import numpy).

-If you follow the hint above, your data is no longer being opened from a

file inside your k mean() function so you may need to tweak it a bit.

-To display the image after you have run k-means, you can use the lines

cv2.imshow(‘Display Window’, manipulated img)

cv2.waitKey(0)

cv2.destroyAllWindows()

-Each pixel is represented by three features: their red, green, and blue

values. You only need to tweak your algorithm to find clusters and then

replace the pixels with the value of their cluster center.

-The more clusters you work with, the slower this algorithm will run, so

for testing it may be useful to work with only 2 clusters.

Here is the starting image:

And here is what your code should return:

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