DSAA311 - Hierarchical Clustering Average Linkage Method Assignment

Assignment Task

Create an interactive Shiny App that explains how clusters get formed using Hierarchical Clustering with the average linkage method to a general audience. The app should avoid technical terminology or jargon without explanation and should instead focus on explaining the concept through visualisation.

• Your app should include:
  • a brief explanation of hierarchical clustering with the average linkage method, 
  • the corresponding dendrogram, 
  • an adjustable control to change the number of clusters, 
  • a visualisation of the resulting clusters, and if possible, of the effect of cutting on the dendrogram given the choice of cluster number, 
  • accompanying text explaining your 
• The principles of good data visualisation should be always maintained, including correct depictions, appropriate labels and titles, be readable, and use various techniques to highlight important trends and features to the audience. 
• Clustering should be performed in a statistically valid 
• A brief paragraph (2-4 sentences) should be also included explaining whether this method is a supervised or unsupervised statistical learning Include a definition of either supervised or unsupervised statistical learning, depending on which method corresponds to hierarchical clustering. 
• Your visualisation and explanation must use the NCI60 data set from the ISLR package as the example data. The data, NCI60_data.csv, NCI60_labs.csv, and a data description can be found on Moodle.
• All data visualisation presented in the Shiny app must be generated by R code and included in your app script.
• Your app script, and all source files must be uploaded as a .zip folder to Moodle. The folder must be able to be compiled.

Create an interactive Shiny App that explains the role of the complexity parameter in a Classification Decision Tree Model to a general audience - not the definition of it, but the role/impact it plays on what happens to a tree when it gets modified. The app should avoid technical terminology or jargon without explanation and should instead focus on explaining the concept through visualisation.

• Your app should include
  • the corresponding decision tree diagram, 
  • an adjustable control to change the complexity parameter, 
  • accompanying text explaining your visualisation, 
  • a brief explanation of classification trees,
  • a brief explanation of tree growing, 
  • a confusion matrix demonstrating test accuracy of the model, with a brief description of how to interpret the results, 
  • a ROC curve and AUC statistic, with a brief description of how to interpret the (
• The principles of good data visualisation should be always maintained, including correct depictions, appropriate labels and titles, be readable, and use various techniques to highlight important trends and features to the audience. 
• Your analysis should be performed in a statistically valid 
• A brief paragraph (2-4 sentences) should be included explaining whether this method is a supervised or unsupervised statistical learning Include a definition of either supervised or unsupervised statistical learning, depending on which method corresponds to classification decision trees. (1 mark)
• All data visualisation presented in the Shiny app must be generated by R code and included in your app script.
• Your visualisation and explanation must be based on the Western Collaborative Group study dataset reference here in epitools package within R. The data, and data description can be found on in R by installing package epitools and typing ?wcgs. It has also been made available as a CSV file in

Extra description is on file “WCGS.pdf” on Moodle. The outcome of interest being Coronary Heart Disease (chd69).

Make sure to make the variables the correct type (e.g., factors).

• Your app script, and all source files must be uploaded as a .zip folder to Moodle. The folder must be able to be compiled.

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