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COMP 527 2019 2 CA Assignment
Data Clustering
Implementing the k-means clustering algorithm
Assessment Information
Assignment Number 2 (of 2)
Weighting 13%
Assignment Circulated 6th March 2019
Deadline 1st April 2019, 15:00 UK Time (UTC)
Submission Mode Electronic via Departmental submission system
Learning outcome assessed (1) A critical awareness of current problems and research
issues in data mining.
Purpose of assessment This assignment assess the understanding of k-means clustering
algorithm by implementing k-means for text clustering.
Marking criteria Marks for each question are indicated under the corresponding
question.
Submission necessary in order No
to satisfy Module requirements?
Late Submission Penalty Standard UoL Policy applies.
1
1 Objectives
This assignment requires you to implement the k-means clustering algorithm using the Python
programming language.
Note that no credit will be given for implementing any other types of clustering
algorithms or using an existing library for clustering instead of implementing
it by yourself. However, you are allowed to use numpy and scipy
libraries for accessing data structures such as numpy.array or scipy.sparse.
But it is not a requirement of the assignment to use numpy or scipy. You
can use matplotlib for plotting but it is not compulsory to use matplotlib. You
must provide a README file describing how to run your code to produce the
results. Programs that do not run will result in a mark of zero!
2 Word Clustering using k-means
In the assignment, you are required to cluster words belonging to four categories: animals, countries,
fruits and veggies. The words are arranged into four different files. The first entry in each line is a
word followed by 300 features (word embedding) describing the meaning of that word.
Questions
(1) Implement the k-means clustering algorithm with Euclidean distance to cluster the instances
into k clusters. (30 marks)
(2) Vary the value of k from 1 to 10 and compute the precision, recall, and F-score for each set of
clusters. Plot k in the horizontal axis and precision, recall and F-score in the vertical axis in
the same plot. (10 marks)
(3) Now re-run the k-means clustering algorithm you implemented in part (1) but normalise each
feature vector to unit `2 length before computing Euclidean distances. Vary the value of k from
1 to 10 and compute the precision, recall, and F-score for each set of clusters. Plot k in the
horizontal axis and precision, recall and F-score in the vertical axis in the same plot. (10
marks)
(4) Now re-run the k-means clustering algorithm you implemented in part (1) but this time use
Manhattan distance over the unnormalised feature vectors. Vary the value of k from 1 to 10
and compute the precision, recall, and F-score for each set of clusters. Plot k in the horizontal
axis and precision, recall and F-score in the vertical axis in the same plot. (10 marks)
(5) Now re-run the k-means clustering algorithm you implemented in part (1) but this time use
Manhattan distance with `2 normalised feature vectors. Vary the value of k from 1 to 10 and
compute the precision, recall, and F-score for each set of clusters. Plot k in the horizontal axis
and precision, recall and F-score in the vertical axis in the same plot. (10 marks)
(6) Now re-run the k-means clustering algorithm you implemented in part (1) but this time use
cosine similarity as the distance (similarity) measure.Vary the value of k from 1 to 10 and
2
compute the precision, recall, and F-score for each set of clusters. Plot k in the horizontal axis
and precision, recall and F-score in the vertical axis in the same plot. (10 marks)
(7) Comparing the different clusterings you obtained in (2)-(6) discuss what is the best setting for
k-means clustering for this dataset. (20 marks)
3 Deadline and Submission Instructions
Deadline for submitting this assignment is 1st April 2019, 15:00 UK time (UTC).
Submit
(a) the source code for all your programs,
(b) a README file (plain text) describing how to compile/run your code to produce the
various results required by the assignment, and
(c) a PDF file providing the answers and graphs for the questions (2)-(8).
Compress all of the above files into a single tar ball (tgz) file and specify the filename as
studentid.tgz. Replace studentid with your student ID. It is extremely important that you
provide all the files described above and not just the source code! (If you are unable to create
a tgz file then create a zip file)
Every year I get assignments that do not mention a name or a student id. Please
check that your submission has these details because otherwise there is no way
to find out who submitted the assignment.
Submission is via the departmental electronic submission system accessible (from within the
department) from
http://intranet.csc.liv.ac.uk…
WX：codehelp
