Explain what is Hierarchical Clustering Algorithm?

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Hierarchical clustering algorithm combines and divides existing groups, creating a hierarchical structure that showcase the order in which groups are divided or merged.

Hierarchical clustering is a popular method used in data analytics and machine learning for grouping similar data points into clusters based on their characteristics. It’s an unsupervised learning algorithm, meaning it doesn’t require labeled data for training.

Here’s a concise explanation of hierarchical clustering:

Hierarchical clustering builds a hierarchy of clusters by either iteratively merging smaller clusters into larger ones (agglomerative) or splitting larger clusters into smaller ones (divisive).

Agglomerative hierarchical clustering, which is more commonly used, starts with each data point as its own cluster and then iteratively merges the closest pairs of clusters until only one cluster remains, resulting in a dendrogram—a tree-like structure that shows the order and distance of the merges.

The process typically involves the following steps:

  1. Compute the proximity matrix: Calculate the distance (or similarity) between each pair of data points.
  2. Merge the closest clusters: Combine the two clusters (or data points) that have the smallest distance according to a chosen linkage criterion. Common linkage criteria include single linkage (minimum distance), complete linkage (maximum distance), and average linkage (average distance).
  3. Update the proximity matrix: Recalculate the distances between the new cluster and all other clusters or data points.
  4. Repeat: Iterate steps 2 and 3 until all data points are clustered together or until a certain stopping criterion is met (e.g., a predetermined number of clusters).

The result is a dendrogram that shows the hierarchical relationship between clusters, allowing users to visually inspect the data’s structure and choose an appropriate number of clusters.

Hierarchical clustering is flexible and doesn’t require specifying the number of clusters beforehand, making it useful for exploratory data analysis. However, it can be computationally expensive for large datasets due to its O(n^3) time complexity, where n is the number of data points.