COMP 527代写、代做Python设计、Python编程语言调试、代写algorithm留学生

日期：2019-03-29 10:34

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.

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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

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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/cgi-bin/submit.pl?module=COMP527.

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