Clustering: Hierarchical Clustering

Hierarchical clustering is a popular unsupervised machine learning technique used to group similar data points into clusters. It builds a tree-like hierarchical structure of clusters, where each data point is initially considered as an individual cluster. These individual clusters are then successively merged based on their similarity until all data points belong to a single cluster.

Types of Hierarchical Clustering

There are two main types of hierarchical clustering:

1. Agglomerative Clustering: This approach starts with each data point as a separate cluster and combines the most similar clusters at each step.
2. Divisive Clustering: This approach begins with all data points in one cluster and splits them into smaller clusters based on dissimilarity.

Steps in Hierarchical Clustering

The process of hierarchical clustering involves the following key steps:

1. Calculate Similarity: Use a distance metric to calculate the distances/similarities between data points.
2. Initial Clustering: Treat each data point as an individual cluster.
3. Merge/Split: Successively merge (agglomerative) or split (divisive) clusters based on similarity/dissimilarity.
4. Construct Dendrogram: Represent the merging/splitting process in a dendrogram for visualization.
5. Cluster Identification: Determine the optimal number of clusters by cutting the dendrogram at an appropriate level.

Advantages of Hierarchical Clustering

• No need to specify the number of clusters beforehand.
• Provides valuable insights into how individual data points are grouped at different levels of granularity.
• Easy to interpret and visualize using dendrograms.

Disadvantages of Hierarchical Clustering

• Computationally intensive for large datasets due to its iterative nature.
• Difficult to apply on very large datasets due to high time complexity.

In conclusion, hierarchical clustering is a powerful technique in unsupervised machine learning that offers flexibility and interpretability in segmenting complex datasets into meaningful groups based on their similarities.