Understanding Hierarchical Clustering in Machine Learning: A Comprehensive Guide
Machine learning is a complex and multifaceted field that requires a deep understanding of algorithms, data structures, and statistical models. One of the key concepts in machine learning is clustering, which is the process of grouping similar objects together. There are several clustering methods such as hierarchical, k-means, density-based, and more. In this article, we’ll focus on hierarchical clustering and provide you with a comprehensive guide on how it works.
What is Hierarchical Clustering?
Hierarchical clustering is an unsupervised learning method where the data is grouped based on their similarity or distance. In this method, we start with every object in its own cluster and then merge them based on similarity criteria. This merging process continues until we reach the desired number of clusters or until each object belongs to a single cluster.
Types of Hierarchical Clustering
There are two types of hierarchical clustering: agglomerative and divisive.
Agglomerative clustering starts by treating each data point as a single cluster and then progressively merges the nearest clusters together until the desired number of clusters is reached.
Divisive clustering starts with all data points in a single cluster and recursively divides the cluster into smaller sub-clusters until we reach the desired number of clusters.
Distance Measures for Hierarchical Clustering
Distance measures are used to evaluate the similarity or dissimilarity between data points. There are several distance measures used in hierarchical clustering such as Euclidean distance, Manhattan distance, and Mahalanobis distance. Euclidean distance is the most commonly used distance measure in hierarchical clustering.
How Does Hierarchical Clustering Work?
Let’s consider a simple example of hierarchical clustering. Suppose we have a dataset of six points as shown in the image below.
We can see that points 1 and 2 are close together, as are points 4 and 5. Point 3 is closer to points 4 and 5 than it is to points 1 and 2.
Using agglomerative clustering, we can start by considering each point as a separate cluster. We can then merge the two closest points, which in this case are points 1 and 2, into a single cluster.
Next, we consider the remaining points as separate clusters and merge the two closest points, which are points 4 and 5.
Finally, we merge the two clusters created in the previous steps (points 1, 2, and 3 and points 4 and 5) to form a single cluster.
At this point, we have achieved our desired number of clusters.
Advantages and Disadvantages of Hierarchical Clustering
Some advantages of hierarchical clustering are that it is intuitive, does not require the number of clusters to be specified in advance, and can easily handle outliers.
However, some disadvantages include that it can be computationally expensive for large datasets, sensitive to initial conditions, and can be difficult to interpret in cases where the data has a complex structure.
Conclusion
Hierarchical clustering is a powerful unsupervised learning method that can be used for a wide range of problems such as image segmentation, text clustering, and more. In this article, we explored hierarchical clustering, types of hierarchical clustering, distance measures, and how it works. We also discussed its advantages and disadvantages. By understanding the basics of hierarchical clustering, you can better utilize its benefits and limitations in your analytical projects.
