Given the following temperatures for the past 6 days: Use K-Means clustering to divide the above temperatures into K=2 clusters (by hand): a) Start from initial cluster centers at c1=21 and c2=29. At each iterations record (1) the cluster to which each point belongs, and (2) the updated cluster centers at the end of iteration. Continue until the clusters are not updated. At the end, calculate the within cluster sum of square (WCSS). b) Repeat the same procedure from initial clusters c1=21 and c2=22. c) What is the difference between the solutions? Which one do you prefer and why? Question 2: Hierarchical Clustering Hierarchical clustering is a clustering technique used to group similar data points into clusters based on their distances or similarities. It builds a hierarchy of clusters, either in a bottom-up (agglomerative) or top-down (divisive) manner. In agglomerative hierarchical clustering, each data point starts as its own cluster and is successively merged with the closest neighboring cluster until all data points belong to a single cluster or the desired number of clusters is achieved. The objective of this question is to introduce hierarchical clustering and guide you through the process of performing hierarchical clustering on 1dimensional data. Please follow the step-by-step instructions given in the pdf file "Learn with an example - Hierarchical Clustering", to perform a hierarchical clustering to the following data points: 8,12,14,18,26,29,31,38,42,47,52 Please include the steps in your submission. Note: Although the given pdf file is self-contained, but feel free to use other available online resources to learn hierarchical clustering, if needed