# IT446- DATA MINING-Assignment help

Question One
Compare K-means and K-medoids algorithms. List down the main differences between these two algorithms?
Question Two
Cluster the following eightpoints into three clusters using K means clustering algorithm and use Euclidean distance.
A1=(2,10), A2=(2,5), A3=(8,4), A4=(5,8),
A5=(7,5), A6=(6,4), A7=(1,2), A8=(4,9).
a) Create distance matrix by calculating Euclidean distance between each pair of points. (0.5 mark)
b) Suppose that the initial centers of each cluster are A1, A4 and A7. Run the k-means algorithm for once only and show:
i. The new clusters (i.e. the examples belonging to each cluster) (1 mark)
ii. The centers of the new clusters (0.5 mark)
Question Three
Define partitioning clustering approaches and hierarchical clustering approaches and give a typical method for each type. Also state the main difference between these two approaches?
Assignment 3
[Total Mark for this Assignment is 4]
Data Mining & Data Warehousing
IT446
Instructions:
This Assignment must be submitted on Blackboard (WORD format only) via the allocated folder.
Email submission will not be accepted.
You are advised to make your work clear and well-presented, marks may be reduced for poor presentation. This includes filling your information on the cover page.
You MUST show all your work, and text must not be converted into an image, unless specified otherwise by the question.
Late submission will result in ZERO marks being awarded.
The work should be your own, copying from students or other resources will result in ZERO marks.
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1 Mark
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2 Marks
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1 Mark
Learning Outcome(s):
LO:3

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