Hierarchical clustering could be a natural next step for further analysis, especially considering the presence of the outliers and the potentially uneven cluster shapes observed in the KMeans model results. Hierarchical clustering can offer insights into the hierarchical structure of the data, potentially capturing nuances that KMeans might overlook.Which of the following is a key difference between KMeans and Hierarchical clustering algorithms?Hierarchical Clustering forms spherical clusters around centroids. Each data point belongs to the cluster with the nearest centroid. In contrast, KMeans builds a hierarchy of clusters, starting with individual data points and gradually merging them into larger clusters based on proximity.Hierarchical clustering assumes clusters to be spherical and of equal size, which may not always reflect the true underlying structure of the data, whereas KMeans can capture clusters of arbitrary shapes and sizes, offering more flexibility in cluster formation.Hierarchical Clustering requires the number of clusters (K) to be specified in advance. In contrast, KMeans does not require the number of clusters to be predefined. It produces a hierarchical tree of clusters, and the number of clusters can be determined after the clustering process.Hierarchical clustering does not require the number of clusters to be predefined. It produces a hierarchical tree of clusters, and the number of clusters can be determined after the clustering process, whereas KMeans requires the number of clusters (K) to be specified in advance.