AiVibeUnsupervised Learning
Unsupervised learning finds structure in data without labelled answers, for example grouping similar items with clustering, reducing many variables to a few with dimensionality reduction, or identifying unusual points for anomaly detection.
Clustering algorithms such as k-means, hierarchical clustering and DBSCAN group observations by similarity. Dimensionality reduction methods such as principal component analysis (PCA) and t-SNE compress many correlated variables into a few components for visualisation or further modelling. Density estimation and autoencoders model what normal data looks like, so that points which fit poorly can be flagged as anomalies.
In industry, unsupervised methods identify machine operating states, group similar faults or alarms, find patterns in process data, compress sensor signals and detect anomalies where labelled failure examples are scarce. Self-supervised learning, a related approach that creates training signals from the data itself, is used to pre-train large language and vision models.
Without labels, results are harder to evaluate: clusters may not correspond to meaningful categories, and the number of clusters or the anomaly threshold must be chosen with domain expertise. Feature scaling strongly affects distance-based methods. Findings are usually validated by engineers or against a small labelled set before they drive decisions.
Key points
- Finds patterns in data without labelled answers
- Main tasks are clustering, dimensionality reduction and anomaly detection
- Useful where labelled failure or defect examples are scarce
- Results need domain validation because no ground truth exists
Where AiVibe comes in
AiVibe delivers AI and machine learning services, chatbots and virtual assistants with RAG, MCP tools and voice, AI quality management including bias detection and model validation, and the AIMURUGA AI agent, and builds Intel-based edge AI devices using the Intel Distribution of OpenVINO toolkit.