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For clustering cases (Euclidean distance and the other similarity measure reported in previous two tasks), visualise the cluster centres using Tag cloud using Python package WordCloud 5 marks Successfully used the WordCloud Package to visualise the cluster centres using at least two different similarity measures. marks uccessfully used the ordCloud Package to isualise the cluster centres sing at least one similarity easu re. t marks Demonstrated knowledge in NordCloud Package and risualisation, but cannot use hem successfully. D mark Failed to show any evidence of knowledge in WordCloud Package and visualisation.

Criteria 3: • Repeat K-means clustering with 5 clusters using a similarity measure other than Euclidean distance. • Evaluate the clustering performance over SO random initializations of K-means using adjusted rand index and adjusted mutual information. • Report the clustering performance and compare it with the results obtained in step 2. i marks successfully completed all three :asks. 3 marks Successfully completed any two of the three tasks. marks uccessfully completed any ne of the three tasks. 0 mark Failed to complete any given task.

Criteria 2: • Perform K-means clustering with 5 clusters using Euclidean distance as similarity measure. • Evaluate the clustering performance using adjusted rand index and adjusted mutual information. • Report the clustering performance averaged over 50 random initializations of K-means. 5 marks Successfully completed all three tasks. 3 marks Successfully completed any two of the three tasks. 2 marks Successfully completed only one of the three tasks. D mark Failed to complete any given task.

Criteria Criteria 1: Reading files corresponding to the feature matrix, class labels and the term dictionary and store them in variables X, true Labels and terms using Python notebook. Excellent S marks successfully read all files and stored in corresponding variables sing Python notebook. Good 3 marks Partially achieved the goal by missing reading or storing one file or variable. Fair 2 marks Only able to either reading files or creating variables in Python to store any value. Unsatisfactory) mark Fail to read and store using Python notebook.