Introduction to Hierarchical Clustering

Introduction to Hierarchical Clustering

• Hierarchical clustering is an unsupervised machine learning technique used to group similar data points together.
• It creates a hierarchy of clusters, where clusters at higher levels are composed of clusters from lower levels.
• It aims to build a hierarchy that best reflects the inherent similarity of data points.

Types of Hierarchical Clustering

• Agglomerative Clustering: This approach starts with each data point as a separate cluster and iteratively merges the closest clusters until a single cluster remains.
• Divisive Clustering: This approach starts with all data points in a single cluster and recursively splits clusters into smaller clusters based on the dissimilarity between data points within the cluster.

Agglomerative Clustering: A Detailed Look

• Similarity Metrics: Agglomerative clustering utilizes similarity metrics to measure the distance between clusters.
  • Common metrics include Euclidean distance, Manhattan distance, and cosine similarity. The choice of metric significantly impacts the results.
• Linkage Criteria:
  • Single Linkage: (Nearest neighbor): Merges the two clusters whose closest data points are the nearest. Can be sensitive to outliers as it might create chained clusters that do not accurately reflect the overall structure.
  • Complete Linkage: (Furthest neighbor): Merges the clusters whose furthest data points are closest to each other. This is less sensitive to outliers than single linkage but can be slower due to having to examine all data points in each cluster.
  • Average Linkage: Merges clusters based on the average distance between all pairs of data points in the two clusters being merged. It provides a balance between single and complete linkage, finding the average resemblance.
• Algorithm Steps:
  • Begin with each data point as a single cluster.
  • Identify the closest pair of clusters using the selected linkage criterion.
  • Merge the identified clusters into a new cluster.
  • Repeat steps 2 and 3 until all data points belong to a single cluster. The result is a hierarchy of clusters.

Divisive Clustering

• Algorithm Steps:
  • Start with all data points in a single cluster.
  • Identify a cluster to be split using the chosen distance metric.
  • Divide the cluster into two sub-clusters that maximize the distance between them, or minimize the distance within the sub-clusters.
  • Repeat until each data point forms its own cluster.

Dendrogram

• A dendrogram is a tree-like diagram that visualizes the hierarchical clustering process.
• It shows the progression of merging or splitting clusters, with the height of the fusion representing the dissimilarity.
• Horizontally, it represents distances between the clusters, and vertically, it represents the hierarchy.

Choosing a Hierarchical Clustering Method

• Data characteristics: The nature of the data (e.g., high dimensionality, presence of outliers) will influence the appropriate similarity metric and linkage criterion.
• Desired structure: The desired structure of the clusters (e.g., whether clusters should be of a specific size or shape) can influence the choice of method.
• Computational resources: Divisive methods can be computationally intensive for large datasets, which suggests using agglomerative methods for better performance when dealing with massive data.

Applications of Hierarchical Clustering

• Customer Segmentation: Grouping customers according to their purchasing behavior or characteristics.
• Document Clustering: Grouping similar documents together in a collection of text documents.
• Image Segmentation: Grouping similar regions in an image.
• Biological Classification: Classifying different species or organisms.

Advantages of Hierarchical Clustering

• Understanding the overall structure of the data
• Visualization using dendrograms
• Ability to uncover different levels of granularity in clusters.

Disadvantages of Hierarchical Clustering

• Computationally intensive, especially for large datasets; this can become a significant limitation.
• Can be sensitive to noisy data.
• Difficult to determine the optimal number of clusters without additional post-processing.

This quiz covers the fundamental concepts of hierarchical clustering, an unsupervised machine learning technique. Explore the two main types: agglomerative and divisive clustering, along with their operational methods and similarity metrics. Gain a deeper understanding of how these clustering techniques create hierarchies of data points.