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Laboratory 6 Data Clustering using K-Means

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Computer Science CS134
Laboratory 6
Data Clustering using K-Means
Objective. Creating and using a class to organize data.
Lab overview. In this project, we will be developing a technique to cluster data. Clustering is
a method to classify data (for example, muscle vs. neuron vs. liver vs. marrow). When data can
be clustered, we can treat disparate points of data as part of a larger, cohesive whole. A clustering
can also be used to identify how to classify new data values encountered in the future.
The K-Means Approach. When confronting a stream of raw, unclassi ed data that has some
\geometric” interpretation, it is often useful to be able to partition the data into classes or clusters
of values. If these data can be interpreted in a geometric manner, it is often useful to think of this
clustering as a spatial segregation of the points.
Two functions help understand the relationships between spatial points: the notion of a distance, and the ability to compute the center-of-mass, or mean of many values. Computing the
distance is important in evaluating the relative closeness of a point to several clusters. The ability
to compute the center-of-mass allows us to, in a sense, label the points of the cluster with a value
that is a good representation of any of them. The approach, here, is cast in spatial terms. More
advanced clustering of higher dimensional data considers the notions of distance and center-of-mass
more abstractly.
In our approach, we will attempt to identify exactly k clusters of data. How we select k is
determined by the application. For example, we may imagine that baseballs thrown at the plate
fall into a small number|4 or 5|di erent categories. In any case, the value k is an input or to the
algorithm. In the end, the data will be segregated into k classes, each of which is labeled by the
center-of-mass of the cluster. If the clustering is good, then the points of the cluster are represented
well by their label; each point is closer to its cluster’s mean than the means of other clusters.
This notion suggests a way we can generally classify new points: the distance is computed from
the point to each cluster center. Each points is then assigned to the cluster whose center is nearest.
If we were given the k labels for the clusters a priori, the algorithm for clustering the data, D
is simple:
Cluster data D, given k labels:
for every point, p, in D:
find the first cluster whose label is closest to p
add p to that cluster
Notice that several cluster centers may be equally close to p. When that happens, we assign p to
the  rst \closest cluster” encountered.
The data itself can be used to \bootstrap” informative classi cations: To begin, we draw k
points, at random, from the input data. Next, we go through every data point, p, and assign
it to the cluster that is nearest. After all of the points have been assigned, we then recompute
the centers|the means|of each newly formed cluster. We iteratively repeat the reclassi cation
around recomputed means until points no longer move between clusters. It frequently does not
require very many repetitions of this process before stability is reached.
Cluster data, D:
Select k labels, L.
repeat:
Cluster D according to L (see above), watching for migration
until data migration becomes sufficiently small.
There can be multiple stable solutions, so the k-means algorithm is typically run a few times
from di erent initial seed points. Frequently the algorithm will converge on the same set of k means
even with di erent initial seeds. Eventually one selects the best solution, the one that minimizes
the total cluster variance (more on that later).
Application. As an example of how k-means clustering might be used, we’ll investigate the
compression of colors in an image. When we read an image from disk, we are able to access the
individual picture elements or pixels as elements of a two-dimensional image array. Each pixel
keeps track of its color, a value that can be encoded using the RGB color model. This model
imagines that the color is generated by mixing three di erent lights in di erent levels of intensity,
much as we might imaging light colors are generated on subject using red, green, and blue stage
lights. If they’re all turned o  (they all have an intensity of zero), the stage is black. If the red
light is turned full on (a high intensity), and the others are o , the stage is red. By mixing these
primary light colors, we can generate new colors. For example, full intensity red, green, and blue
lights will generate a white light. Thus, pixel colors are imagined as points found in a cube from
color space whose dimensions are red, green, and blue intensities. To make manipulation of these
values easy, the intensities are often stored as bytes|integers between 0 and 255, inclusive.
It is often useful to reduce the total number of colors used to represent an image. For example,
if we reduced the number of colors used in Da Vinci’s Mona Lisa with a selection of 8, we could
generate a manageable paint-by-number image. If we wanted to compress an image’s storage, we
could replace the 24 bits needed to store a full-spectrum color with 3 bits which would identify
one of 8 select colors. The 8 colors would be stored in a very small, 24 byte table saved with the
image. The image would then take about 1
8
th of the space!
Code Review. Clone the lab resources from the gitlab repository, as usual:
git clone https://evolene.cs.williams.edu/cs134-labs/22xyz3/lab06.git ~/cs134/lab06
As usual, we begin by carefully reading through the existing code. Your job is to  ll out the code
in cluster.py. We have also provided an application that will re-color an image using k colors,
determined by clustering the image’s original colors.
We have provided only a skeleton of the Clustering class. The methods that are present were
designed to provide the functionality we expect to use in the image recoloring application. While
you are responsible for designing and implementing the internals of this class, you may not change
the method headers.
To help you with the design process, read through the documentation of the method headers we
have provided. You might also look at the image-recoloring application to see how the Clustering
methods will be used. You should be thinking:
? What di erent types of information are stored inside the Clustering object? This state
information will help you understand the attributes that will support the class.
? The application makes method calls to access information inside the Clustering. Which
types of information are directly stored in attributes? Is there information that can be
computed on-the- y?
? What are the constraints on the ability of an application to modify state in the object?
Obviously, when the clustering is constructed, the state is constructed, as well; what data
is passed to the initializer? Does it ever change? If it does change, which methods can be
identi ed as mutator methods?
With the above information in mind, review slots , the list of names of attributes that must
be maintained in the structure. These attributes are initialized in the init method. Notice,
by the way, that the initializer will be passed methods to compute distance between objects and
the mean of several objects.
Once you have perused the  les, check out the introductory video associated with this project.
Required Tasks. The focus of this week’s project will be the development of the Clustering
class. Here’s what needs to be done:
1. In init , implement a better way to select the k initial labels from the data. You might
consider randint, choice, or shuffle methods from the random module. Document your
thinking by including appropriate comments.
2. Now, implement the map method. This method is responsible for determining the index of a
value’s ideal cluster.
3. Suppose your clustering produces k (possibly empty) labeled clusters. Complete the relabel
method to update the label of any non-empty cluster to be mean of the values in the cluster.
Labels for empty clusters can be left as is.
4. Implement recluster. This method is called to re ne the current clustering. Keep track
of (and return) the number of values that migrate to a new cluster. This information is
necessary to help the algorithm identify when the clustering process has stabilized.
5. Return to init and make sure that it uses the methods you’ve just written to construct
a stable clustering.
6. When cluster.py is run as a script, it generates plots of clusterings of points. Run the
script a few times. You will likely observe cases where the clusters are well assigned. You
may observe cases where a mean sits between two clusters and one cluster contains two means.
7. Once you are con dent your class is working, try running the image recoloring application.
We’ve included Van Gogh’s Bedroom (a small image) and Irises (a large image). The result
is found in idResult.png. Does it seem to work?
8. When you are  nished with your implementation sign the honor code. Then, add, commit,
and push your changes to cluster.py and honorcode.txt.
Extras.
1. Peruse recolor.py. It begins by clustering all the pixels in the image. Then, when the
image is needed, after classi es each image pixel’s color by mapping it to one of the k
labels. Modify the recoloring code to print the k labels to the output. Make sure you submit
your changes to recolor.py for grading.
2. Develop a property, variance(), of Clustering that computes the variance (the average
squared Pythagorean distance) of every point from that point’s cluster center:
var =


X
k−1
i=0
X
p∈clusteri
distance(p, centeri)
2

 /n
Observe that the variance is lower when the centers are well assigned.
3. For more credit, you can explore how the variance depends on your choice of k. Increasing
k gives you more centers, so should reduce the typical distance (and, thus, distance-squared)
to the centers. Experiment with a clustering and tabulate how the variance depends on the
parameter k. The elbow of the resulting plot is an aid in deciding how to pick k. Add,
commit, and push a plot called elbow.pdf that demonstrates this relationship.
?

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