When to Use Hierarchical Clustering: A Guide for Data Analysts

If you are working with a large dataset and looking to group similar observations together, clustering algorithms can be a powerful tool. One popular type of clustering algorithm is hierarchical clustering, which creates a tree-like structure of nested clusters.

This technique is particularly useful when the underlying data has some sort of hierarchy, such as taxonomies or evolutionary relationships.

One of the advantages of hierarchical clustering is that it does not require you to specify the number of clusters in advance. Instead, the algorithm builds a hierarchy of clusters, allowing you to explore different levels of granularity and choose the number of clusters that best fits your needs.

However, it is important to note that hierarchical clustering can be computationally expensive, especially for large datasets, as it requires computing the pairwise distance between all observations.

So when should you use hierarchical clustering? If you have a dataset with a clear hierarchy, or if you want to explore different levels of granularity without committing to a specific number of clusters, hierarchical clustering may be the right choice for you.

However, if you have a very large dataset or if computational efficiency is a top priority, you may want to consider other clustering algorithms that are better suited for your needs.

What is Hierarchical Clustering?

When it comes to clustering, hierarchical clustering is a popular method used in data mining and statistics. It is a technique that seeks to build a hierarchy of clusters by iteratively grouping or separating data points.

The result is a tree-like structure called a dendrogram, which explains the relationship between all the data points in the system.

There are two types of hierarchical clustering: Agglomerative clustering and Divisive clustering. Agglomerative clustering is a bottom-up approach where each data point is assumed to be a separate cluster at first.

Then, the algorithm iteratively merges the two closest clusters until all data points belong to a single cluster. Divisive clustering, on the other hand, is a top-down approach where all data points are initially considered as a single cluster. Then, the algorithm recursively splits the cluster into smaller ones until each data point is in its own cluster.

One of the benefits of hierarchical clustering is that it provides a visual representation of the relationship between data points, making it easier to interpret and understand the results.

Additionally, hierarchical clustering does not require the number of clusters to be specified in advance, unlike other clustering techniques such as K-means clustering.

Image source: Rohit Tandon | Unsplash

Types of Hierarchical Clustering

There are two types of hierarchical clustering: agglomerative and divisive clustering. Both of these methods are used to group data points together based on their similarities and differences.

Agglomerative Hierarchical Clustering

Agglomerative hierarchical clustering is a bottom-up approach that starts with each data point in its own cluster and then merges the closest clusters together. This process continues until all data points are in the same cluster. The similarity between two clusters is based on a distance metric, such as Euclidean distance or correlation distance.

Agglomerative hierarchical clustering is widely used in various fields, including biology, computer science, and social sciences. It is particularly useful when the number of clusters is not known in advance, and the dendrogram can help identify the optimal number of clusters.

Divisive Hierarchical Clustering

Divisive hierarchical clustering is a top-down approach that starts with all data points in one cluster and then recursively splits it into smaller clusters. The similarity between two clusters is based on a distance metric, such as Euclidean distance or correlation distance.

Divisive hierarchical clustering is less commonly used than agglomerative clustering, but it can be useful in certain situations. For example, if the number of clusters is known in advance, divisive clustering can be used to create a specific number of clusters.

Overall, hierarchical clustering is a powerful tool for grouping data points together based on their similarities and differences. By understanding the different types of hierarchical clustering, you can choose the appropriate method for your specific data set and analysis.

When to Use Hierarchical Clustering

If you have a dataset and you are wondering whether hierarchical clustering is an appropriate method to use, there are a few factors to consider. Here are some key sub-sections to consider:

Number of Clusters

Hierarchical clustering is useful when you do not know the number of clusters in your dataset. It is also useful when you want to explore the dataset and identify any patterns or subgroups. If you have a dataset with a clear outcome variable to predict, you may want to use other clustering methods, such as k-means clustering.

Dendrogram

Hierarchical clustering creates a dendrogram, which is a tree-like diagram that shows the relationships between the clusters. The dendrogram can help you visualize the hierarchy of the clusters and identify any subgroups or outliers. The dendrogram can also help you determine the appropriate number of clusters to use.

Distance Metric

The choice of distance metric can affect the results of hierarchical clustering. The most commonly used distance metrics are Euclidean distance and Manhattan distance. Euclidean distance is useful when the data is continuous and has a normal distribution, while Manhattan distance is useful when the data is categorical or binary. Other distance metrics, such as Pearson correlation or cosine similarity, may be used depending on the type of data being clustered.

Observations

Hierarchical clustering is useful for datasets with a small to moderate number of observations. If you have a large dataset, hierarchical clustering may be computationally expensive and other clustering methods may be more appropriate.

Numeric Vs Categorical Data

Hierarchical clustering can be used for both numeric and categorical data. However, the choice of distance metric may depend on the type of data being clustered. For numeric data, Euclidean distance is commonly used, while for categorical data, Manhattan distance or other distance metrics may be used.

Overall, hierarchical clustering is a useful method for exploring and identifying patterns in your dataset. It is particularly useful when you do not know the number of clusters in your dataset and want to explore the data.

However, the choice of distance metric and other parameters can affect the results, so it is important to carefully consider these factors when using hierarchical clustering.

What Are the Pros and Cons of Hierarchical Clustering?

Advantages

Hierarchical clustering is a powerful technique that has a number of advantages over other clustering methods. Here are some of the advantages:

• Robustness: Hierarchical clustering is more robust than other methods since it does not require a predetermined number of clusters to be specified. Instead, it creates hierarchical clusters based on the similarity between the objects, which makes it more reliable and accurate.
• Detailed information: Hierarchical clustering provides detailed information about which observations are most similar to each other. This level of detail is not provided by many other algorithms, which generally just return the ID of the cluster a given observation belongs to.
• Flexibility: Hierarchical clustering is a flexible technique that can be used with a wide range of distance measures and linkage methods. This makes it suitable for a variety of clustering problems.

Disadvantages

Despite its many advantages, hierarchical clustering also has some limitations that you should be aware of:

• Computational complexity: Hierarchical clustering can be computationally expensive, especially for large datasets. This is because the algorithm needs to calculate the distance between every pair of data points, which can be time-consuming.
• Arbitrary decisions: When using hierarchical clustering, it is necessary to specify both the distance metric and the linkage criteria. There is rarely any strong theoretical basis for such decisions, which makes the technique of dubious relevance in modern research.
• Sensitivity to noise: Hierarchical clustering is sensitive to noise and outliers in the data. This can lead to the creation of clusters that are not meaningful or useful.

Overall, hierarchical clustering is a powerful technique that can be used to identify meaningful patterns in complex datasets. However, it is important to be aware of its limitations and to carefully consider the specific problem you are trying to solve before using this technique.

Agglomerative Hierarchical Clustering

Agglomerative hierarchical clustering is a popular method used to group objects in clusters based on their similarity. This method is also known as AGNES (Agglomerative Nesting) and is a bottom-up approach. In this approach, each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.

Bottom-Up Approach

The bottom-up approach in agglomerative hierarchical clustering is also known as the “agglomerative” approach. It is called bottom-up because each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.

The algorithm starts by treating each object as a singleton cluster and then successively merges pairs of clusters until all clusters have been merged into a single cluster that contains all objects. This approach is often used when the number of objects is small.

Distance Metrics

Distance metrics are used to calculate the distance between two clusters. The most commonly used distance metric is the Euclidean distance, which measures the distance between two points in n-dimensional space.

Other distance metrics include Manhattan distance, which measures the distance between two points by summing the absolute differences of their coordinates, and cosine similarity, which measures the similarity between two vectors in terms of the cosine of the angle between them.

Dendrogram

A dendrogram is a tree-like diagram that shows the hierarchical relationship between clusters. It is often used to visualize the results of agglomerative hierarchical clustering.

The dendrogram starts with all the objects in their own clusters and then successively merges pairs of clusters until all clusters have been merged into a single cluster that contains all objects.

For example, a dendrogram plot in Matlab

The height of each branch in the dendrogram represents the distance between the clusters being merged at that step.

Overall, agglomerative hierarchical clustering is a powerful tool for grouping objects based on their similarity. By using distance metrics and dendrograms, you can visualize the results of clustering and gain insights into the structure of your data.

Comparison with K-Means Clustering

When it comes to clustering analysis, two of the most popular methods are hierarchical clustering and K-means clustering.

While both methods have their own advantages and disadvantages, it is important to understand the differences between them to determine which method is best suited for your data. Here is a comparison between hierarchical clustering and K-means clustering.

Number of Clusters

One of the main differences between hierarchical clustering and K-means clustering is the number of clusters. With K-means clustering, you need to specify the number of clusters before running the algorithm.

In contrast, hierarchical clustering does not require a pre-specified number of clusters, and the number of clusters can be determined based on the structure of the data.

Scale

Another difference between hierarchical clustering and K-means clustering is the scale of the data. K-means clustering works best with data that is on a similar scale, while hierarchical clustering can handle data that is on different scales.

This is because hierarchical clustering uses a distance-based approach, which can handle data with different units and scales.

Patterns

When it comes to detecting patterns in the data, hierarchical clustering is often better suited for non-linear patterns, while K-means clustering is better suited for linear patterns.

This is because K-means clustering assumes that the clusters are spherical and evenly sized, while hierarchical clustering can handle clusters of different shapes and sizes.

Variables

Another difference between hierarchical clustering and K-means clustering is the type of variables that can be used. K-means clustering works best with continuous variables, while hierarchical clustering can handle a variety of variable types, including categorical variables.

Speed

K-means clustering is generally faster than hierarchical clustering, especially for large datasets. This is because K-means clustering only needs to calculate the distance between each point and the cluster center, while hierarchical clustering needs to calculate the distance between each point and every other point in the dataset.

What is the advantage of hierarchical clustering over K-means clustering?

One advantage of hierarchical clustering over K-means clustering is that it does not require a pre-specified number of clusters. This means that you can explore the structure of the data and determine the number of clusters based on the data itself.

Another advantage is that hierarchical clustering can handle data with different scales and variable types, which can be useful in certain situations.

Hierarchical Clustering Vs Spectral Clustering?

When it comes to clustering algorithms, there are many different types to choose from. Two popular options are hierarchical clustering and spectral clustering. In this section, we’ll explore the differences between these two methods and when you might choose one over the other.

What is Spectral Clustering?

Spectral clustering is a method that uses the eigenvectors of a similarity matrix to cluster data points. It works by first constructing a similarity matrix based on the pairwise distances between data points.

This matrix is then transformed into a Laplacian matrix, which is used to compute the eigenvectors. The eigenvectors are then used to cluster the data points into groups.

When would you use hierarchical clustering over spectral clustering?

While spectral clustering can be a powerful tool, there are some situations where hierarchical clustering may be a better choice:

• Small datasets: Hierarchical clustering can be a good choice for smaller datasets where the number of data points is not too large. Spectral clustering can be computationally expensive and may not be practical for small datasets.
• Data with a clear hierarchical structure: Hierarchical clustering is well-suited for data that has a clear hierarchical structure, such as taxonomies or family trees. Spectral clustering does not take this structure into account and may not produce clusters that align with the hierarchy.
• Data with noise or outliers: Hierarchical clustering can be more robust to noise and outliers in the data, as it uses a bottom-up approach to clustering. Spectral clustering can be sensitive to noise and outliers and may produce clusters that are not meaningful.

Overall, the choice between hierarchical clustering and spectral clustering will depend on the specific characteristics of your data and the goals of your analysis. It’s important to carefully consider the strengths and weaknesses of each method before making a decision.

Summary: When To Use Hierarchical Clustering

When deciding whether to use hierarchical clustering for your dataset, there are a few key factors to consider. Hierarchical clustering is particularly useful when:

• You do not have a clear outcome variable to predict
• You want to detect patterns in your data
• Your data has some sort of hierarchy
• You have a small to medium-sized dataset

One of the advantages of hierarchical clustering is that it can reveal the underlying structure of your data, which can be useful for exploratory data analysis. Additionally, hierarchical clustering can be more interpretable than other clustering methods, as it produces a dendrogram that shows the relationships between clusters.

However, hierarchical clustering can be computationally intensive and may not be suitable for large datasets. It is also important to choose the appropriate linkage method and distance metric for your data, as different methods can produce different results.

Overall, hierarchical clustering is a powerful tool for exploratory data analysis and can be particularly useful when you do not have a clear outcome variable to predict. However, it is important to carefully consider the characteristics of your dataset and choose the appropriate method for your analysis.

FAQ: Hierarchical Clustering