Which of the following algorithms is commonly used for hierarchical clustering?Agglomerative clusteringExpectation-Maximization (EM)DBSCANK-Means

The algorithm commonly used for hierarchical clustering is Agglomerative clustering.

Here's a step-by-step explanation:

1. Agglomerative clustering is a type of hierarchical clustering method. Hierarchical clustering, as the name suggests, creates a hierarchy of clusters.

2. In Agglomerative clustering, each data point is initially considered as an individual cluster.

3. Then, at each iteration, the two clusters that are the closest are combined together. This process is repeated until only a single cluster is left.

4. The result is a tree-like diagram called a dendrogram, representing the nested grouping of patterns and similarity levels at each step of the clustering process.

5. Other algorithms like Expectation-Maximization (EM), DBSCAN, and K-Means are not typically used for hierarchical clustering. EM is more associated with Gaussian mixture models, DBSCAN is a density-based clustering algorithm, and K-Means is a type of partitioning clustering.