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Assignment 3: Clustering

Four datasets (Iris, YeastGene, Example, Utilities) can be found on Piazza. In each dataset, each
row corresponds to an object and each column corresponds to an attribute. The attribute values
are comma-separated. You don’t need to do normalization for any of the datasets.
The initial centroids to be used in K-means for Iris and YeastGene datasets are provided. In each
file, each row denotes the initial centroid of a cluster.
In this assignment, you are asked to implement K-means algorithm and Agglomerative
algorithm (using Min to define inter-cluster distance). Templates (kmeans_template.py,
hierarchical_template.py) are for Python 3.

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Assignment 3: Clustering

Four datasets (Iris, YeastGene, Example, Utilities) can be found on Piazza. In each dataset, each
row corresponds to an object and each column corresponds to an attribute. The attribute values
are comma-separated. You don’t need to do normalization for any of the datasets.
The initial centroids to be used in K-means for Iris and YeastGene datasets are provided. In each
file, each row denotes the initial centroid of a cluster.
In this assignment, you are asked to implement K-means algorithm and Agglomerative
algorithm (using Min to define inter-cluster distance). Templates (kmeans_template.py,
hierarchical_template.py) are for Python 3.
In kmeans_template.py, you are asked to fill in two functions: assignCluster and
getCentroid. In assignCluster, each object is assigned to the cluster whose centroid is the
closest to the object, based on Euclidean distance. In getCentroid, cluster centroids are
updated based on current assignment.
In hierarchical_template.py, you are asked to fill in merge_cluster and update_distance.
In merge_cluster, you need to merge two closest clusters. In update_distance, you need
to update the distance matrix after the merging using Min as the inter-cluster distance.
You cannot directly call a function or package that implements K-means algorithm and
Agglomerative algorithm. You need to implement these algorithms by yourself. If you
are not sure about whether it is OK to use a certain function, please post your question on
Piazza.
Please take the following steps:
1. Implement K-means algorithm as follows:
Repeat T times:
 For each object xi
 Calculate Euclidean distance between xi and each of the K centroids
 Assign xi to the cluster whose centroid is the closest to xi
 For each cluster
 Calculate its centroid as the mean of all the objects in that cluster
2. Implement Hierarchical clustering algorithm (with Min as inter-cluster distance
definition):
 Obtain the distance matrix by computing Euclidean distance between each pair of
objects
 Let each object be a cluster (Assign the cluster index as 1 to N, where N is the
number of objects)
 Set the current index as T=N+1
 Repeat
 Find the smallest entry in the distance matrix—suppose the entry is i-th
row and j-th column
 Merge the clusters that correspond to the i-th row and j-th column of the
distance matrix as a new cluster with index T
 Remove the rows and columns of the two old clusters and add new row
and column for the new cluster to the distance matrix by computing the
distance between the new cluster and each of the remaining clusters
 T=T+1
 Until only one cluster remains
3. Test your K-means on Iris dataset. Use the provided initial centroids, and set the
number of iterations (T) as 12, and the number of clusters (K) as 3. The final cluster
centroids should be:
Cluster 1: 5.006,3.418,1.464,0.244
Cluster 2: 6.8538,3.0769,5.7154,2.0538
Cluster 3: 5.8836,2.741,4.3885,1.4344
4. After running the filled kmeans_template.py code file, you can obtain a data file Iris_
kmeans_cluster.csv, which contains the Iris data and the assigned clusters. Then, apply
the PCA and plotting functions you used in Assignment 1 to project Iris data to 2
dimensions and draw the scatter plot. Use different colors for different clusters in the
scatter plot. The plot should look like the one below:
5. If you get the correct final cluster centroids and the plot, then repeat steps 3 and 4 on
the YeastGene dataset. Use the provided initial centroids, and set the number of iterations
(T) as 7, and the number of clusters (K) as 6.
6. Apply Hierarchical Clustering algorithm implemented in Step 2 on the Example
dataset. The order of the merging should be:
1 2 7
3 7 8
4 5 9
6 9 10
8 10 11
Each row here denotes an operation of merging two clusters to form a new cluster. The
first two indices denote the clusters to be merged, and the last one denotes the index of
the new cluster.
7. If you get the correct order in Step 6, then apply the hierarchical clustering algorithm
on the Utilities dataset.
8. Prepare your submission. Your final submission should be a zip file named as
Assignment3.zip. In the zip file, you should include:
 A folder “Code”, which contains all the codes used in this assignment. You are
required to fill in the template.
 Report: A doc or pdf file named as Assignment3.doc or Assignment3.pdf. The
report should consist of the following parts: 1) The cluster centroids obtained on
YeastGene dataset after the T iterations. 2) The scatter plot obtained on YeastGene
dataset after applying PCA and plotting points using different colors for different
clusters. 3) The order of merging in the hierarchical clustering on the Utilities
dataset. 4) The codes of your K-means and hierarchical clustering algorithm
implementation.
9. Log in any CSE department server and submit your zip file as follows:
submit_cse469 Assignment3.zip
Please refer to Course Syllabus for late submission policy and academic integrity policy. We will
take the submission time recorded by the server as the time of your submission. This assignment
must be done independently. Running your submitted code should be able to reproduce the results
in your report.