Description
Outline
The goal of this group project assignment is to obtain practical knowledge
of the clustering algorithms by using the scikit-learn classifier. You will
apply the Gaussian Mixture Models Clustering algorithm and the k-Means
clustering algorithm on the same dataset and compare the results.
You can read about Gaussian Mixture Models and how to use them
in scikit-learn here http://scikit-learn.org/stable/modules/mixture.
html
Dataset
A dataset, named Seeds Dataset of unlabelled data is available at https:
//archive.ics.uci.edu/ml/datasets/seeds. Your task is to apply both
the Gaussian Mixture Models and the k-Means Clustering algorithms on
this dataset. Fine-tune the parameters of the algorithms, to the best of your
ability, until you get clusters that you are happy with. Compare the results.
You will also need to visualise the obtained clusters in a 2D scatter plot.
You may need to reduce the dimensionality of the clustered data points for
the purposes of visualisation.
What to submit
All documents should be compressed in a zip file.
• The code in a digital form. Do not submit the data sets.
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• A small text file on how to run the code. A necessary condition for
the assignment to be admissible is that the examiner can run the code
(regardless of operating system). If the examiner is unable to run the
code 0 points will be given for the entire assignment!
• Between 1000 – 5000 characters of report describing how the clustering
algorithms comparatively performed on the task. State your opinions
as to why the results were as such. The report should also include a
visualisation of the clustered data in a 2D scatter plot. You may want
to reduce the dimensionality of your data before you visualise it.
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