What is the main difference between hierarchical clustering and partitioning clustering?Partitioning clustering builds clusters by recursively splitting or merging them.Hierarchical clustering assigns each data point to exactly one cluster.Partitioning clustering is based on a hierarchy of clusters.Hierarchical clustering does not require the number of clusters to be specified in advance.

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.

Explain the concept of hierarchical clustering. Compare and contrastagglomerative and divisive hierarchical clustering. Discuss theapplications of hierarchical clustering in real-world situations.

What is a key characteristic of hierarchical clustering?Answer areaIt requires the number of clusters to be specified in advanceIt can be visualized using a dendrogramIt is a partitional clustering methodIt is always faster than K-Means

What is the main difference between classification and clustering? Explain using concrete examples.

Which of the following is a type of hierarchical clustering?Answer areaK-MeansDBSCANAgglomerative clusteringMean Shift