Hierarchical clustering is an unsupervised machine learning technique used to group similar data points together based on their distance or similarity.

Unlike k-means clustering, which requires a predefined number of clusters, hierarchical clustering does not require specifying the number of clusters in advance. Instead, it creates a tree-like structure called a dendrogram that represents the relationships between data points and clusters at different levels of granularity.

There are two main approaches to hierarchical clustering:

  • Agglomerative (bottom-up): This method starts with each data point as a separate cluster and iteratively merges the closest clusters together until all points belong to a single cluster.
  • Divisive (top-down): This method starts with all data points in a single cluster and recursively splits the cluster into smaller subclusters until each data point forms its own cluster or a desired number of clusters is reached.

    In the context of SEO and keyword research, hierarchical clustering can be used to:

    • Identify keyword themes: By clustering keywords based on their semantic similarity, hierarchical clustering can reveal hierarchical relationships between keywords and help identify broader themes and subtopics within a keyword dataset.
    • Inform content structure: The hierarchical structure of keyword clusters can guide the organization and structure of a website’s content, ensuring that topics are covered comprehensively and in a logical manner.
    • Analyze competitor keywords: Hierarchical clustering can be applied to analyze the keywords that competing websites rank for, uncovering insights into their content strategies and the relationships between the topics they cover.
    • Keyword mapping: By visualizing the hierarchical relationships between keywords, SEO professionals can create detailed keyword maps that inform content planning, site architecture, and internal linking strategies.

      Like k-means clustering, hierarchical clustering is not an SEO-specific technique but can be a valuable tool for organizing and understanding large keyword datasets.

      The choice between k-means and hierarchical clustering depends on the specific requirements of the project, such as the desired level of granularity, the need for a fixed number of clusters, and the interpretability of the results.

      In practice, SEO professionals may use a combination of clustering techniques, as well as other keyword research and analysis methods, to gain a comprehensive understanding of their keyword landscape and inform their SEO strategies.