How Hierarchical Clustering Works
Hierarchical clustering is an unsupervised learning technique used to group similar data points into clusters. It builds a hierarchy of clusters that allows for a comprehensive understanding of the data structure. The process can be divided into two main approaches: agglomerative and divisive.
Agglomerative Clustering
This is the most common method. It starts with each data point as an individual cluster and iteratively merges the closest pairs of clusters until only one cluster remains or a predefined number of clusters is achieved. The merging process relies on a distance metric, such as Euclidean distance, and linkage criteria, such as single-linkage, complete-linkage, or average-linkage.
Divisive Clustering
In contrast to agglomerative clustering, divisive clustering begins with all points in one cluster and recursively splits it into smaller clusters. This method is less common due to its computational complexity but can provide detailed insights into data structure.
Dendrogram Representation
Hierarchical clustering results can be visually represented using a dendrogram, which is a tree-like structure. The dendrogram shows the arrangement of clusters and the distances at which clusters are merged or split, providing insights into the similarities within the dataset.
Overall, hierarchical clustering is a powerful tool for exploratory data analysis, allowing teams to identify patterns and structure in unlabelled data sets effectively.