What is Hierarchical Clustering?
Hierarchical clustering is a popular unsupervised learning technique in the field of machine learning, primarily used for grouping similar objects into clusters. This method constructs a hierarchy of clusters, creating a tree-like structure known as a dendrogram. Unlike other clustering methods, hierarchical clustering does not require a pre-specified number of clusters, making it versatile for exploratory data analysis.
Types of Hierarchical Clustering
There are two main types of hierarchical clustering:
- Agglomerative Clustering: This is a bottom-up approach. It starts with each data point as a separate cluster and iteratively merges the closest clusters until a single cluster is formed or a specified number of clusters is reached.
- Divisive Clustering: This is a top-down approach. It begins with a single cluster containing all data points and recursively splits it into smaller clusters.
Applications
Hierarchical clustering is widely used across various domains, including biology for phylogenetic tree construction, marketing for customer segmentation, and image analysis for object recognition. Its ability to reveal the underlying structure of data makes it a vital tool in data analysis.
Conclusion
Overall, hierarchical clustering is a robust technique that provides valuable insights into data patterns, making it an essential aspect of unsupervised machine learning in the broader context of artificial intelligence.