A Clear Guide to Hierarchical Clustering in Machine Learning

Introduction to Hierarchical Clustering

Machine learning offers various methods to group data, and hierarchical clustering is one of the most intuitive for discovering patterns in datasets. Unlike algorithms that require a predefined number of groups, hierarchical clustering constructs a tree of clusters, allowing you to determine the number of clusters later. It’s widely used in fields like biology, customer analysis, and text classification, especially when relationships aren’t immediately obvious. This guide explains how hierarchical clustering functions, its main types, comparisons with other methods, and its strengths and limitations. Our aim is to make this method clear and practical for application.

How Does Hierarchical Clustering Work?

Hierarchical clustering groups data by forming a tree of relationships, gradually creating smaller or larger clusters step by step. It operates in two primary ways:

1. Agglomerative Clustering: This common method starts with each data point in its own group, then merges the two closest groups. This process continues until all data points belong to one cluster.
2. Divisive Clustering: Starting with a single cluster containing all data points, this method splits them into smaller clusters step by step.

What makes hierarchical clustering flexible is its method of measuring “closeness” between clusters. Different linkage methods like single, complete, average, and Ward’s method influence the clustering outcome. The results are visualized as a dendrogram, a tree diagram that shows how clusters merge. By selecting different heights on the dendrogram, you can choose the number of clusters that best fits your data.

Types of Hierarchical Clustering

There are two main types of hierarchical clustering:

• Agglomerative Clustering: Builds clusters from the bottom up and is more efficient for varied datasets. However, it cannot undo previous merges, which can lead to suboptimal groupings.
• Divisive Clustering: Begins with all data points as one cluster and splits them into smaller clusters. It’s theoretically capable of finding better solutions but is slower computationally.

Hierarchical clustering differs from flat clustering methods like k-means by producing a hierarchy rather than a fixed number of groups. This hierarchy is useful for data that naturally forms multiple levels of grouping or when exploring different numbers of clusters without rerunning the algorithm.

Comparing Hierarchical Clustering to Other Methods

Unlike k-means clustering, hierarchical clustering does not require specifying the number of clusters beforehand. K-means performs well with spherical, balanced clusters but struggles with irregular shapes. Hierarchical clustering’s flexibility allows for post hoc decisions about the number of clusters.

Another alternative, DBSCAN, groups data based on density and can identify noise points. While effective for varied shapes, it requires careful parameter selection. Hierarchical clustering focuses on building a tree structure without assumptions about cluster shapes.

Strengths and Limitations

Strengths

• Interpretability: The dendrogram provides a clear visual representation of cluster relationships, accessible even to non-experts.
• Flexibility: The ability to decide the number of clusters after running the algorithm is a practical advantage.

Limitations

• Scalability: It is computationally intensive on large datasets.
• Sensitivity to Outliers: Outliers can distort the dendrogram structure.
• Irreversible Merges: Once clusters are merged, they cannot be separated, which can lead to poor fits.

The choice of linkage method significantly affects the results. Exploring different linkage strategies and their dendrograms can help find the best fit for your data.

Conclusion

Hierarchical clustering is a powerful method for revealing hidden data structures by forming nested groups. Its ability to demonstrate cluster relationships at various levels makes it particularly useful for exploratory analysis. While it may not scale well to very large datasets and is sensitive to outliers, its interpretability and flexibility offer significant advantages in practical applications. By understanding how to build and represent clusters, and by selecting the appropriate distance and linkage criteria, you can effectively apply hierarchical clustering to uncover meaningful patterns in your data.