Hierarchical Clustering Hierarchical clustering is an unsupervised machine learning technique that groups data points based on their similarity. It involves...
Hierarchical Clustering
Hierarchical clustering is an unsupervised machine learning technique that groups data points based on their similarity. It involves building a hierarchy of clusters, with each cluster being a subset of the next higher-level cluster. This process is known as agglomerative because it gradually merges clusters together until the data points are grouped into as few as possible clusters.
How it works:
Initialisation: Start with a set of data points spread out randomly in the feature space.
Merging: Find pairs of data points that are similar to each other.
Cluster expansion: Gradually merge the clusters together, starting with the pairs that are most similar to each other.
Final clusters: Continue this process until all the data points are grouped into a set of clusters that are as large as possible.
Benefits of hierarchical clustering:
Unsupervised learning: It can be used for data points with no labelled features.
Easy to interpret: The clusters can be represented visually using a dendrogram.
Preserves relationships: It takes into account the distances between data points, which can be important for certain applications.
Examples:
Market segmentation: Companies can use hierarchical clustering to group customers based on their purchase history and demographics.
Medical diagnosis: Healthcare providers can use it to group patients with similar medical conditions.
Image segmentation: Computer vision systems can use hierarchical clustering to segment images into different objects