Unsupervised learning conducted by artificial intelligence systems involves using algorithms and techniques that enable them to identify and learn patterns, relationships, and groupings without requiring labelled data or any explicit guidance.

AI systems can explore data and discover hidden patterns, relationships, and groupings using a variety of techniques, including:

Clustering algorithm:

• Which groups similar data points together based on properties. Its main goal is to identify groups of data points that share common characteristics or exhibit similar behaviour.
• Examples of clustering algorithms are k-means, hierarchical clustering, and density-based clustering.

Dimensionality Reduction:

• It reduces the number of variables or data sets while preserving essential information.
• Techniques like Principal Component Analysis (PCA), t-SNE (t-Distributed Stochastic Neighbour Embedding), and Autoencoders help achieve this.

Anomaly Detection:

• It identifies rare or unusual patterns in data that are different from usual by learning the behaviour of data and then detecting instances where the behaviour differs.
• It demonstrates its use in fraud detection, network intrusion detection, or equipment failure prediction.

Association Rule Learning:

• It discovers interesting relationships or associations between different variables in a dataset. It then identifies patterns of co-occurrence or dependencies among those variables.
• The Apriori algorithm is a well-known example of association rule learning.

Generative Modelling:

• It understands the underlying distribution of data.
• Models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) can learn to generate new data samples that resemble the training data.
• It is beneficial in situations like data generation, image synthesis (the process of generating or synthesizing pictures computationally), or anomaly detection.
• It is beneficial in situations like data generation, image synthesis (the process of generating or synthesising pictures computationally), or anomaly detection.