How do I evaluate a model with no labels?

With internal metrics and judgement, in that order. The silhouette score measures how tightly grouped each cluster is relative to its distance from other clusters, and works without ground truth. Davies-Bouldin and Calinski-Harabasz do similar jobs. But all of them measure geometric properties, not usefulness, so a high silhouette score on clusters nobody in the business can name is not a success. Visualising the result and checking whether the groups correspond to something real is the step you cannot skip.

Is unsupervised learning really more computationally expensive?

Not as a rule, and the claim deserves more nuance than it usually gets. K-means on a moderate dataset is fast and PCA is essentially a matrix decomposition. What is genuinely expensive is t-SNE, which scales poorly with sample count, and training generative models like GANs, which is often far more costly than a supervised classifier on the same data. So the cost depends on the algorithm, not on the absence of labels. The thing you reliably save is the labelling itself, which is often the most expensive part of a supervised project.

How do I choose the number of clusters for K-means?

You do not get to avoid choosing, which is the honest starting point. The elbow method plots within-cluster variance against k and looks for where the improvement flattens; the silhouette method picks the k with the best average score. Both are heuristics and they sometimes disagree. If the number matters to a decision, run several values, look at what each grouping actually contains, and pick the one that produces groups someone can act on. Algorithms like DBSCAN infer the count from density instead, which suits data where clusters are irregularly shaped.

When would I use an autoencoder for anomaly detection?

When normal behaviour is plentiful and anomalies are rare or unknown in advance. You train the autoencoder to compress and reconstruct normal examples, so it becomes good at exactly that and bad at everything else. Reconstruction error then becomes your anomaly score: something the model cannot rebuild accurately is unlike what it was trained on. That framing suits fraud and equipment failure, where you have huge amounts of normal data and few labelled examples of the thing you are looking for.

Are generative models really unsupervised?

In the classical framing yes, because they learn a data distribution without labels telling them what anything is. The line has blurred, though. Modern generative systems are frequently trained with self-supervised objectives, where the label is derived from the data itself, such as predicting a masked word, and then refined with human feedback that is explicitly supervised. Calling them unsupervised is accurate about where the field started and increasingly loose about how they are actually built.

Journal AI Integration January 21, 2023

AI Integration · Machine Learning

Unsupervised Learning: Techniques, Types, and Applications

Understand unsupervised learning methods including clustering, dimensionality reduction, anomaly detection, and generative models with practical examples.

Anurag Verma

3 min read

Unsupervised Learning Clustering Machine Learning PCA Data Science

Unsupervised Learning: Techniques, Types, and Applications

Key takeaways

Unsupervised learning works without labels, so the goal shifts from predicting a known answer to finding structure nobody has annotated.
Four families cover most of it: clustering (K-means, hierarchical), dimensionality reduction (PCA, t-SNE), anomaly detection (one-class SVM, autoencoders), and generative models (VAEs, GANs).
The workflow is the same three steps as supervised learning, prepare, train, evaluate, but evaluation is the hard one because there is no ground truth to score against.
The silhouette score is the usual clustering metric: it measures how much closer a point is to its own cluster than to the next nearest one.
K-means needs you to choose the number of clusters in advance, which is the assumption doing most of the work in any K-means result.
The real limitation is interpretation. An algorithm will always return clusters; whether they correspond to anything meaningful is a judgement you make, not something the model reports.

Unsupervised learning is a type of machine learning where the model is not provided with labeled data and is instead expected to find patterns and relationships in the input data on its own. It is used to discover hidden structures in the data and can be used for tasks such as clustering, dimensionality reduction, and anomaly detection.

The process of unsupervised learning typically involves three main steps:

Data preparation: This step involves cleaning, transforming, and organizing the input data so that it is in a format that can be used by the model.

Model training: In this step, the model is trained on the input data using an unsupervised learning algorithm. The goal of the algorithm is to find patterns and relationships in the data, such as clusters of similar data points or low-dimensional representations of the data.

Model evaluation: In this step, the model’s performance is evaluated by assessing how well it has learned the underlying patterns and relationships in the data. This can be done by visualizing the results, calculating metrics such as the silhouette score, or by applying the model to new data to see how well it generalizes.

The specific algorithm used for unsupervised learning depends on the type of problem that needs to be solved. For example, clustering algorithms such as K-means or hierarchical clustering are used for grouping similar data points together, while dimensionality reduction algorithms such as PCA or t-SNE are used for reducing the number of features in the data. Anomaly detection algorithms such as one-class SVM and Autoencoder are used for identifying data points that do not conform to the expected pattern.

There are several types or classifications of unsupervised learning:

Clustering: This involves grouping similar data points together, for example, grouping customers with similar purchasing habits. K-means and Hierarchical clustering are examples of clustering algorithms.

Dimensionality reduction: This involves reducing the number of features in the data while maintaining the important information. PCA (Principal Component Analysis) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are examples of dimensionality reduction algorithms.

Anomaly detection: This involves identifying data points that do not conform to the expected pattern. One-class SVM and Autoencoder are examples of anomaly detection algorithms.

Generative models: These models learn the probability distribution of the data and can generate new data samples that are similar to the input data. Examples include Variational Autoencoder and Generative Adversarial Networks (GANs)

Note: There are many others also…

Advantages of unsupervised learning include:

It can discover hidden patterns and structures in the data that might not be immediately obvious.
It can be used to reduce the dimensionality of the data, making it easier to visualize and understand.
It can be used to identify anomalies or outliers in the data.

Disadvantages of unsupervised learning include:

It can be difficult to evaluate the performance of an unsupervised model, as there is no clear measure of success.
It can be more computationally expensive than supervised learning, as the model must explore the entire dataset to find patterns.
It can be difficult to interpret the results of unsupervised models, as the patterns and relationships discovered may not be immediately understandable to humans.

Frequently asked questions

How do I evaluate a model with no labels?: With internal metrics and judgement, in that order. The silhouette score measures how tightly grouped each cluster is relative to its distance from other clusters, and works without ground truth. Davies-Bouldin and Calinski-Harabasz do similar jobs. But all of them measure geometric properties, not usefulness, so a high silhouette score on clusters nobody in the business can name is not a success. Visualising the result and checking whether the groups correspond to something real is the step you cannot skip.
Is unsupervised learning really more computationally expensive?: Not as a rule, and the claim deserves more nuance than it usually gets. K-means on a moderate dataset is fast and PCA is essentially a matrix decomposition. What is genuinely expensive is t-SNE, which scales poorly with sample count, and training generative models like GANs, which is often far more costly than a supervised classifier on the same data. So the cost depends on the algorithm, not on the absence of labels. The thing you reliably save is the labelling itself, which is often the most expensive part of a supervised project.
How do I choose the number of clusters for K-means?: You do not get to avoid choosing, which is the honest starting point. The elbow method plots within-cluster variance against k and looks for where the improvement flattens; the silhouette method picks the k with the best average score. Both are heuristics and they sometimes disagree. If the number matters to a decision, run several values, look at what each grouping actually contains, and pick the one that produces groups someone can act on. Algorithms like DBSCAN infer the count from density instead, which suits data where clusters are irregularly shaped.
When would I use an autoencoder for anomaly detection?: When normal behaviour is plentiful and anomalies are rare or unknown in advance. You train the autoencoder to compress and reconstruct normal examples, so it becomes good at exactly that and bad at everything else. Reconstruction error then becomes your anomaly score: something the model cannot rebuild accurately is unlike what it was trained on. That framing suits fraud and equipment failure, where you have huge amounts of normal data and few labelled examples of the thing you are looking for.
Are generative models really unsupervised?: In the classical framing yes, because they learn a data distribution without labels telling them what anything is. The line has blurred, though. Modern generative systems are frequently trained with self-supervised objectives, where the label is derived from the data itself, such as predicting a masked word, and then refined with human feedback that is explicitly supervised. Calling them unsupervised is accurate about where the field started and increasingly loose about how they are actually built.

Newer dispatch

Reinforcement Learning: A Comprehensive Introduction

AI Integration

Older dispatch

Calculus for Data Science: An Introduction with Python

AI Integration

Join the conversation.

Comments are powered by GitHub Discussions. Sign in with your GitHub account to leave a comment.