Clustering US Technology Stocks

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.

Describe suitable methods for undertaking hierarchical cluster analysis. In particular, explain how different distance measures and$/$or clustering methods might alter the results and how choice of the number of clusters might be made.

The datafile ustech$2.$ csv consists of weekly closing prices $($USD$)$ from January $2020$ to May $2024$ on the $20$ largest technology companies appearing in the Nasdaq $100$ index.

i$.$ Explore the data, individual securities and their characteristics.

ii$.$ Use hierarchical cluster analysis to determine if meaningful groups can be formed based on weekly stock returns.

iii. What conclusions $($if any$)$ do you draw from your analyses?

What are the main disadvantages of cluster analysis? What do you deduce from your above analyses in light of these considerations?

The solution needs to be in R Not python