Cluster analysis is a technique used in unsupervised machine learning to group a set of objects in such a way that objects in the same group or cluster are more similar to each other than to those in other clusters. 1. Describe suitable methods for undertaking hierarchical and non-hierarchical cluster analysis. In particular, explain how different distance measures and/or clustering algorithms might alter the results and how choice of the number of clusters might be made. [35 marks] 2. The datafile ustech.csv consists of daily closing prices (USD) from January 2019 to June 2021 on 21 technology companies appearing in the Nasdaq 100 index. i. Explore the data, individual securities and their characteristics. ii. Use cluster analysis to determine if meaningful groups can be formed. Pay particular attention to data scaling, i.e. consider carefully if some form of data standardization is necessary. iii. What conclusions (if any) do you draw from your analyses? [45 marks] 3. What are the main disadvantages of cluster analysis? What do you deduce from your above analyses in light of these considerations? [20 marks]