The K-Means and K-Medians Clustering Algorithms - IT Assignment Help

Assignment Task

Objectives
This assignment requires you to implement the k-means and k-medians clustering algorithms using the Python programming language. NOTE 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 library for accessing data structures such as numpy.array. But it is not a requirement of the assignment to use numpy. 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 re-produce your results. Programs that do not run will result in a mark of zero!
 

Assignment description
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 that you will find in the archive CA2data.zip. The first entry in each line is a word followed by 300 features (word embedding) describing the meaning of that word.
 

Questions/Tasks
1. Implement the k-means clustering algorithm to cluster the instances into k clusters. (This is part 1)
2. Implement the k-medians clustering algorithm to cluster the instances into k clusters.
3. Run the k-means clustering algorithm you implemented in part (1) to cluster the given instances. Vary the value of k from 1 to 9 and compute the B-CUBED precision, recall, and F-score for each set of clusters. Plot k in the horizontal axis and the B-CUBED precision, recall and F-score in the vertical axis in the same plot.
4. Now re-run the k-means clustering algorithm you implemented in part (1) but normalise each object (vector) to unit l2 length before clustering. Vary the value of k from 1 to 9 and compute the B-CUBED precision, recall, and F-score for each set of clusters. Plot k in the horizontal axis and the B-CUBED precision, recall and F-score in the vertical axis in the same plot.

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