Explain Hierarchical Clustering in Machine Learning and Its Types
Written By
The IoT Academy
Published onSeptember 16th, 2024
Updated onSeptember 17, 2024
In the vast and dynamic world of machine learning, clustering algorithms play a pivotal role in data analysis, pattern recognition, and information retrieval. Among these, hierarchical clustering in machine learning is widely used due to its interpretability and flexibility in building nested clusters. In this comprehensive guide, we will explore hierarchical clustering, its types, advantages, disadvantages, and its distinction from other clustering algorithms such as K-means. We will also look into examples, use cases, and frequently asked questions.
What is Hierarchical Clustering in Machine Learning?
Hierarchical clustering is a machine learning method used to group similar data points into clusters, forming a tree-like diagram called a dendrogram. Unlike other clustering methods, it doesn’t need you to set the number of clusters beforehand. It builds clusters either from the bottom up (agglomerative) by merging similar points or from the top down (divisive). By splitting a big cluster into smaller ones. This hierarchical clustering in machine learning method helps explore data and is often used in fields like analyzing genes, organizing documents, or grouping customers in marketing.
Key Concepts in Hierarchical Clustering:
Dendrogram: A tree-like structure used to represent how data points cluster.
Linkage Criterion: The method of measuring the distance between clusters.
Distance Metrics: Measures like Euclidean or Manhattan distance are used to compute similarity.
Types of Hierarchical Clustering in Machine Learning
Hierarchical clustering can be broadly divided into two types:
1. Agglomerative Hierarchical Clustering
This is a bottom-up method where each data point begins as its cluster. The algorithm gradually combines similar clusters one by one, creating larger clusters. It keeps merging until all data points are in one cluster or until a specific stopping point is reached.
Single Linkage: This hierarchical clustering in machine learning method merges clusters based on the minimum distance between two data points from different clusters.
Complete Linkage: Here, clusters are merged based on the maximum distance between two data points.
Average Linkage: This method uses the average distance between all pairs of data points in two clusters.
2. Divisive Hierarchical Clustering
Divisive clustering uses a top-down method, starting with all data points in one big cluster. The algorithm then splits the cluster into smaller groups until each data point is in its cluster. While agglomerative clustering is more popular because it is simpler. As well as divisive clustering works well when it makes sense to start with large groups and break them down.
Hierarchical Clustering Example in Machine Learning
Let’s go through an example to see how hierarchical clustering works. Imagine a dataset with measurements of different flowers. Each row shows a different flower, and the columns show features. Like petal length, petal width, sepal length, and sepal width.
We apply hierarchical clustering in machine learning to this dataset to group flowers with similar characteristics into clusters.
Steps in the Hierarchical Machine Learning Model
Start with each flower as its cluster.
Calculate the distance between each pair of flowers using Euclidean distance.
Merge the two closest clusters based on the chosen method (like average distance).
Keep merging clusters until all flowers are grouped into one cluster or until you reach a stopping point.
By plotting the dendrogram, we can observe the relationships between clusters and decide the number of clusters based on the cutting point in the tree.
Hierarchical Clustering in ML Advantages and Disadvantages
Like any algorithm, hierarchical clustering has its pros and cons. Let’s discuss these in detail.
Advantages
Easy to Understand: The dendrogram helps visualize how clusters are formed, making it easier to interpret relationships between data points.
No Need to Pick Number of Clusters: Unlike K-means, you don’t need to decide the number of clusters in advance with hierarchical clustering.
Good for Small Datasets: Hierarchical clustering in machine learning works well for small to medium datasets where understanding detailed relationships matters more than speed.
No Shape Limits: Unlike K-means, which assumes clusters are round, hierarchical clustering doesn’t have any rules about cluster shape.
Disadvantages
Slow for Large Data: Hierarchical clustering in ML can be slow and use a lot of memory for large datasets because it has to calculate distances between all data points.
Sensitive to Outliers: It can give inaccurate results if the data has noise or outliers.
Hard to Handle Big Data: As the dataset gets larger, hierarchical clustering becomes harder to scale and less practical for very big datasets.
Hierarchical Clustering Algorithm in Machine Learning Use Cases
hierarchical clustering in machine learning is widely used in various domains such as biology, marketing, image processing, and more.
Biology: Researchers generally use hierarchical clustering to group genes with similar patterns or classify species based on genetic data.
Market Segmentation: Businesses use it to divide customers into groups based on their buying habits, demographics as well as interests.
Image Segmentation: It helps find similar areas in images by grouping pixels with similar colors.
Document Classification: It groups documents with similar topics or sentiments in natural language processing.
Hierarchical Clustering vs Kmeans Clustering
When it comes to clustering techniques, hierarchical clustering and K means clustering are often compared due to their popularity in machine learning tasks. Both have their strengths, but they differ in approach and application.
Feature
Hierarchical Clustering
K-means Clustering
Approach
Builds a tree-like structure
Partitions data into K clusters
Cluster Shape
No assumption on cluster shape
Assumes spherical clusters
Cluster Number
Determined by dendrogram or cutoff
A predefined number of clusters (K)
Computational Complexity
Higher due to pairwise distances
Lower, as it converges faster
Data Size
Works better with smaller datasets
Works well with large datasets
Conclusion
In conclusion, hierarchical clustering in machine learning is a useful and easy-to-understand method for grouping data points and seeing their relationships. It creates a tree-like diagram, making it great for exploring data in different fields. It doesn’t need you to set the number of clusters in advance and can handle clusters of any shape. However, it can be slow and not work well with noisy data. Comparing it with K-means, we see that both methods have their strengths and are best suited for different situations. Choosing the right method depends on your data and your desire.
Frequently Asked Questions (FAQs)
Q. What is the difference between flat clustering and hierarchical clustering?
Ans. Flat clustering, like K-means, puts data into a set number of clusters without showing how they relate. Hierarchical clustering, however, creates a tree-like structure that shows how clusters are related. It also allows for more detailed and layered analysis.
Q. Is DBSCAN hierarchical clustering?
Ans. No, DBSCAN is not a hierarchical clustering method. It groups data based on how close points are to each other, creating clusters of any shape. While some hierarchical methods can work with density-based approaches, they are quite different from each other.
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