What is the difference between clustering and classification?

Clustering is unsupervised: it groups similar data points together without predefined labels. Classification is supervised and assigns data points to categories that are already known in advance. Use clustering when you don't know the groups ahead of time, and classification when you do.

Which clustering algorithm should I start with?

K-Means is the standard starting point for well-separated, roughly spherical clusters because it's fast and simple to tune. Switch to DBSCAN or OPTICS if your clusters have irregular shapes or your data contains noise and outliers you want the algorithm to ignore.

Can clustering algorithms detect anomalies?

Yes. Anomaly detection is one of the most common uses of clustering: points that don't fit well into any cluster, or that end up isolated in low-density regions, are treated as outliers or potential anomalies.

Do clustering algorithms need labeled data?

No. That's the defining feature of clustering: it works on unlabeled data by grouping points based on similarity alone, which makes it useful when labeling data would be expensive or impractical.

When should I avoid clustering algorithms?

Avoid clustering when the categories in your data are already well-defined and understood. In that case, a supervised approach like decision trees or support vector machines will generally perform better than trying to rediscover known categories through unsupervised grouping.

Journal AI Integration February 6, 2023

AI Integration · Machine Learning

Clustering Algorithms: Types, Applications, and Use Cases

A guide to clustering algorithm types — partition-based, hierarchical, density-based, and model-based — with use cases and selection criteria.

Anurag Verma

5 min read

Clustering Machine Learning K-Means DBSCAN Hierarchical Clustering Data Science

Clustering Algorithms: Understanding Types, Applications, and When to Use Them

Key takeaways

Clustering is unsupervised: it groups data points by similarity without needing labeled data, which makes it useful when the structure of a dataset isn't already known.
Use clustering when relationships between data points are unclear; use supervised methods like decision trees or SVMs instead when classes are already well-defined.
The four main families are centroid-based (K-Means, K-Medians), hierarchical (agglomerative, divisive), density-based (DBSCAN, OPTICS), and distribution-based (Gaussian Mixture Models).
Density-based methods like DBSCAN handle clusters of arbitrary shape better than centroid-based methods, which assume roughly spherical clusters.
Common applications include image segmentation, customer segmentation, anomaly detection, document clustering, and fraud detection.

Clustering Algorithms: An Overview

Clustering is a fundamental concept in machine learning and data mining, where the goal is to partition a set of data points into groups (also known as clusters) based on their similarity. Clustering algorithms are unsupervised learning techniques, meaning that they work without the use of labeled data. This makes them a powerful tool for segmenting and organizing large datasets, where the relationships between the data points are not well-defined or understood.

Clustering algorithms have a wide range of applications, including image segmentation, market segmentation, pattern recognition, document clustering, and more. In this article, we’ll take a closer look at clustering algorithms and explore their use cases, benefits, and limitations. If you want a broader view of the unsupervised learning family clustering belongs to, see our guide to unsupervised learning techniques. scikit-learn’s clustering module documentation is a solid reference for implementation details on each algorithm covered below.

When to Use Clustering Algorithms

Clustering algorithms are typically used when the relationship between data points is not well-defined or understood. This makes them ideal for large datasets where it can be difficult to identify the underlying patterns and relationships. Clustering algorithms can be used to segment the data into smaller, more manageable groups, which can then be further analyzed to gain insights into the underlying structure of the data.

Another advantage of clustering algorithms is that they do not require labeled data. This makes them a useful tool for applications where labeled data is not available, or where the cost of labeling the data is prohibitively high.

Clustering algorithms are also commonly used in anomaly detection. In this scenario, the goal is to identify data points that do not fit within the normal distribution of the data. Clustering algorithms can be used to identify these outliers by partitioning the data into clusters based on similarity, and then identifying the data points that do not belong to any of the clusters.

Where Not to Use Clustering Algorithms

While clustering algorithms are a powerful tool for segmenting large datasets, they are not suitable for all applications. Clustering algorithms are not well-suited for datasets where the relationships between the data points are well-defined and understood. In these cases, supervised learning algorithms, such as decision trees or support vector machines, may be a better choice.

Additionally, clustering algorithms are not well-suited for applications where there is a clear definition of the classes or categories that the data points belong to. In these cases, classification algorithms may be a better choice — our guides to decision trees and support vector machines cover two common supervised options.

Types of Clustering Algorithms

There are several types of clustering algorithms, each with its own strengths and limitations. Some of the most commonly used clustering algorithms include:

Centroid-based Clustering: Centroid-based clustering algorithms, such as K-Means and K-Medians, are based on the idea of finding the center of each cluster. The algorithm starts by randomly initializing the centroids, and then iteratively updates the centroids by finding the mean or median of the data points in each cluster. See our dedicated walkthrough of centroid-based clustering for a closer look at how K-Means converges.
Hierarchical Clustering: Hierarchical clustering algorithms, such as Agglomerative and Divisive, construct a hierarchical tree-like structure to represent the relationships between the data points. The tree can be represented in either a top-down (divisive) or bottom-up (agglomerative) manner.
Density-based Clustering: Density-based clustering algorithms, such as DBSCAN and OPTICS, define clusters as areas of high density surrounded by areas of low density. These algorithms are particularly useful for finding clusters of arbitrary shape.
Distribution-based Clustering: Distribution-based clustering algorithms, such as Gaussian Mixture Model (GMM), assume that the data points are generated from a mixture of probability distributions. The algorithm estimates the parameters of these distributions, and then uses them to identify the clusters.

Applications of Clustering Algorithms

Clustering algorithms have a wide range of applications, and some of the most common include:

Image Segmentation: Image segmentation is the process of partitioning an image into multiple segments or regions, each of which corresponds to a different object or part of the image. Clustering algorithms can be used to segment an image based on color, texture, or other features.
Customer Segmentation: Customer segmentation is the process of dividing a customer base into groups of individuals that have similar characteristics. This information can be used by businesses to develop targeted marketing strategies, improve customer satisfaction, and increase sales.
Anomaly Detection: Anomaly detection is the process of identifying data points that do not fit within the normal distribution of the data. Clustering algorithms can be used to identify these outliers by partitioning the data into clusters based on similarity, and then identifying the data points that do not belong to any of the clusters.
Document Clustering: Document clustering is the process of organizing and summarizing a large collection of text documents. Clustering algorithms can be used to group similar documents together, allowing users to quickly identify and access relevant information.
Fraud Detection: Fraud detection is the process of identifying fraudulent activities in financial data. Clustering algorithms can be used to identify unusual patterns of behavior, such as large purchases or unusual transactions, which may indicate fraud.

Conclusion

Clustering algorithms are a powerful tool for segmenting and organizing large datasets. They are widely used in a variety of applications, including image segmentation, market segmentation, pattern recognition, and more. Understanding the different types of clustering algorithms and when to use them matters for choosing the right algorithm for a particular problem. Whether you’re working with customer data, financial data, or images, clustering algorithms offer a valuable tool for uncovering the underlying structure and relationships within your data.

Frequently asked questions

What is the difference between clustering and classification?: Clustering is unsupervised: it groups similar data points together without predefined labels. Classification is supervised and assigns data points to categories that are already known in advance. Use clustering when you don't know the groups ahead of time, and classification when you do.
Which clustering algorithm should I start with?: K-Means is the standard starting point for well-separated, roughly spherical clusters because it's fast and simple to tune. Switch to DBSCAN or OPTICS if your clusters have irregular shapes or your data contains noise and outliers you want the algorithm to ignore.
Can clustering algorithms detect anomalies?: Yes. Anomaly detection is one of the most common uses of clustering: points that don't fit well into any cluster, or that end up isolated in low-density regions, are treated as outliers or potential anomalies.
Do clustering algorithms need labeled data?: No. That's the defining feature of clustering: it works on unlabeled data by grouping points based on similarity alone, which makes it useful when labeling data would be expensive or impractical.
When should I avoid clustering algorithms?: Avoid clustering when the categories in your data are already well-defined and understood. In that case, a supervised approach like decision trees or support vector machines will generally perform better than trying to rediscover known categories through unsupervised grouping.

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