代做Classifier and Cluster Analysis in Data Science

University of Canberra
Faculty of Science and Technology
Programming for Data Science G (11521)
Assignment 1
Classifier and Cluster Analysis in Data Science
Due dates: 23:59 Sunday (Week 8)
Type: Individual assignment
Mark for assessment: 20
Submission: Submit a .zip file containing all Python files (.py) in your project via Canvas site.
Late submission: 5% of the total mark per day (1 mark per day). Information on how to apply for
extension can be found in the unit outline on Canvas.
Remarks: As per unit outline, you will need an aggregate of at least 40 over all assignments to pass
the unit.
[6 marks] Question 1: Implement a Python program for Nearest Neighbour Classifier that can
classify an unknown data sample to one of the given classes.
For example, there are 2 classes Red and Blue, and x is an unknown data sample (i.e., we do not
know x is red or blue). After calculating all distances between x and all data samples in the 2 classes,
we find a data sample in the Red class that has shortest distance to x, so x is classified as a red data
sample.
Requirements: Your program reads data samples from 2 text files for 2 classes and unknown data
samples from another text file, runs the Nearest Neighbour Classifier algorithm as demonstrated in
the screenshots below, and outputs all unknown data samples and their classified label to screen
and to another text file. Your program should work with any data dimension D > 1 and any number
of unknown data samples > 0. For Python programming, use a tuple to store a data sample, a list to
store all data samples, and modules to store functions. The main program includes only function
calls and does not include any function implementations. Please do not use other versions of
Nearest Neighbour Classifier you can find on websites or research articles, and do not import any
external packages (except tkinter) to this project.
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[14 marks] Question 2: Implement a Python program for K-Means Clustering that can group data
samples to clusters.
For example, you are given a set of data samples to group them into 2 clusters. The K-means
clustering algorithm generates 2 cluster centres at random, groups data samples that are nearest to
the first cluster centre to form a cluster then do the same with the second one to form another
cluster. The algorithm will generate new cluster centres by averaging data samples in the same
cluster. If the difference between the 2 old cluster centres and the 2 new cluster centres are not
significant, the algorithm will stop, otherwise it removes the old cluster centres and re-groups data
samples for the new cluster centres as seen above to form new clusters. The process repeats until
the difference between the old and new cluster centres is not significant.
Requirements: Your program reads data samples from a text file, runs K-means Clustering algorithm
as demonstrated in the screenshots below, and outputs all data samples with cluster centres to
screen as below. Your program should work with any data dimension D > 1 and any number of
clusters K > 1. For Python programming, use tkinter to display data samples and cluster centres on a
canvas, a tuple to store a data sample or a cluster centre, a list to store all data samples or all cluster
centres, and modules to store functions. The main program includes only function calls and does not
include any function implementations. Please do not use other versions of K-Means Clustering that
you can find on websites or research articles to implement this project. Please do not import any
external packages (except tkinter) to this project.
The screenshots below explain how K-means Clustering algorithm works.
Unknown data
sample
Blue data
samples
Red data
samples
How to classify this unknown data sample?
Step 1. Calculate all distances between this
unknown data sample to all other data samples
Step 2. Find the minimum distance Step 3. Output result: the unknown data
sample is red
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Below is an example of data samples drawn on screen before and after applying K-means clustering.
Page 4 of 4
More details of the above algorithms and demos will be given in lectures and tutorials from Week 2
to Week 7.
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