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  • Megan Silvey

Hierarchical Clustering



In the vast landscape of machine learning algorithms, hierarchical clustering stands out as a versatile and intuitive method for uncovering patterns within data. Whether you're exploring the relationships between biological organisms, segmenting customers based on purchasing behaviors, or analyzing textual data for document clustering, hierarchical clustering offers a powerful toolset. In this blog post, we'll embark on a journey to understand hierarchical clustering, its principles, techniques, and practical applications.


Hierarchical clustering is a method of cluster analysis that builds a hierarchy of clusters. Unlike other clustering techniques like k-means, hierarchical clustering doesn't require the number of clusters to be specified beforehand. Instead, it organizes the data points into a hierarchical tree, also known as a dendrogram, where each node represents a cluster.


There are two main types of hierarchical clustering:

  1. Agglomerative Hierarchical Clustering: This bottom-up approach starts with each data point as its cluster and iteratively merges the most similar clusters until all points belong to a single cluster.

  2. Divisive Hierarchical Clustering: This top-down approach begins with all data points in a single cluster and divides them into smaller clusters recursively until each data point is in its own cluster.


The agglomerative hierarchical clustering process typically involves the following steps:

  1. Start with each data point as its cluster.

  2. Compute the proximity matrix, which represents the pairwise distances between clusters.

  3. Merge the closest clusters based on a chosen linkage criterion (e.g., single-linkage, complete-linkage, average-linkage).

  4. Update the proximity matrix and repeat steps 2-3 until only one cluster remains.


Hierarchical clustering finds applications across various domains, including:

  1. Biology: Clustering genes, proteins, or species based on genetic or phenotypic similarities.

  2. Marketing: Segmenting customers based on demographics, purchasing behaviors, or preferences.

  3. Text Mining: Grouping documents or words with similar content for document clustering or topic modeling.

  4. Image Analysis: Clustering pixels or image regions for tasks like image segmentation or object detection.


Hierarchical clustering offers a powerful and intuitive approach to exploring patterns within data, providing insights into hierarchical structures and relationships between data points. By understanding its principles and techniques, data scientists can leverage hierarchical clustering to gain valuable insights across a wide range of applications. Whether you're a beginner or an experienced practitioner, hierarchical clustering remains an indispensable tool in the data scientist's arsenal.

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