Unlocking the Secrets of Hierarchical Clustering: Finding Patterns in Data

Hierarchical clustering is a technique used in data analysis and unsupervised machine learning to group similar data points together into clusters, forming a hierarchy of clusters. It does so by recursively dividing or merging clusters based on the similarity or dissimilarity between data points. The result is often visualized as a dendrogram, a tree-like diagram that represents the hierarchy of clusters.

How Does Hierarchical Clustering Work?

Hierarchical clustering is a method of grouping similar data points into clusters in a hierarchical fashion. It involves the use of linkage methods to determine how clusters are merged or divided at each step of the clustering process. The main steps involved in hierarchical clustering include:

1. Initialization: At the beginning, each data point is treated as a separate cluster, resulting in as many clusters as there are data points.
2. Iterative Process: The clustering algorithm proceeds iteratively, merging or dividing clusters based on their similarity or dissimilarity. The choice of linkage method is crucial in determining how this similarity is measured.
3. Dendrogram Construction: As clusters are merged or divided, a dendrogram is constructed to visualize the hierarchy of clusters. The dendrogram represents the sequence of merging or splitting operations, allowing us to explore the relationships between data points at…