Unsupervised learning
Unsupervised learning is a machine learning method working without given answers or labels. The model receives only input data and discovers structures, patterns and relationships in it by itself. Typical tasks are clustering, anomaly detection and dimensionality reduction. Hidden groupings can thus be uncovered without knowing in advance what is being looked for.
Also known as: unsupervised learning, unsupervised ML
How does unsupervised learning work?
Unlike supervised learning, in unsupervised learning the algorithm gets no given target values. There are no correct answers for the model to follow. Instead it analyses the structure of the data itself and looks for similarities, groupings or anomalies.
The algorithm looks at the data points' features and assesses how similar or different they are. From that analysis it derives which data points belong together and which fall outside the pattern. The result is a structuring of the data that was not apparent before.
Clustering: sorting data into groups
The best-known application of unsupervised learning is clustering. Data points are grouped by their similarity into so-called clusters. A classic example is customer segmentation: a model groups customers with similar buying behaviour without those groups being defined in advance.
Widespread methods are k-means, hierarchical clustering and DBSCAN. Such clusters help companies understand their audiences better, personalise marketing or develop products more deliberately. The groups found do have to be interpreted sensibly afterwards, though.
Anomaly detection and dimensionality reduction
Besides clustering, anomaly detection is an important application. Here the model identifies data points deviating clearly from normal behaviour. That is used in fraud detection, network security or quality control, to spot unusual events early.
Dimensionality reduction in turn reduces the number of features in a dataset without losing the essential information. Methods such as principal component analysis help present large, complex data more clearly, lower compute needs and make patterns visible.
Benefits and limits of the method
Unsupervised learning's great advantage is that no laboriously labelled data is needed. That saves time and money and allows analysis of data volumes for which manual labelling would be unrealistic. The method can also uncover unknown relationships people might overlook.
At the same time judging the results is harder, since there is no unambiguously correct solution. The structures found have to be checked and interpreted by experts. Whether a cluster is actually meaningful cannot be decided by mathematical measures alone.
Unsupervised learning in your company
Unsupervised learning shows its value above all in exploratory data analysis. It is excellent for understanding large data holdings, forming customer segments or spotting anomalies in processes long before clear questions are formulated.
At Elisabit we help companies put their data to profitable use, choose suitable analytical methods and interpret the results correctly. Raw data thus becomes concrete insight enabling sound decisions and deliberate improvements.
Frequently asked questions
What is the difference from supervised learning?
When Supervised learning the model learns from labelled data with known answers in order to make predictions. In unsupervised learning those labels are absent entirely and the model discovers structures itself. Supervised learning solves prediction tasks, unsupervised learning serves pattern recognition and data exploration.
What is clustering?
Clustering is an unsupervised learning technique in which data points are grouped by their similarity. These groups, the clusters, are not defined in advance but formed by the algorithm itself. A common example is segmenting customers by similar behaviour.
What is unsupervised learning used for?
Typical fields of use are customer segmentation in marketing, anomaly detection in fraud prevention and cybersecurity, and dimensionality reduction to simplify data. It is useful wherever hidden structures in large data volumes are to be uncovered.
Does unsupervised learning need labelled data?
No, and that is precisely its advantage. Unsupervised learning works without manually labelled training data and can work straight from raw data. That saves considerable effort but at the same time makes objective assessment of the results harder, since no known correct solution exists.
Related terms
Machine learning enables systems to learn from data and make predictions without being explicitly programmed.
Supervised learning is machine learning with labelled training data, for classification and regression.
Reinforcement learning lets an agent learn optimal behaviour through trial, error and reward.
Deep learning uses deep neural networks to recognise complex patterns in large volumes of data automatically.
An embedding is a numerical vector representation of meaning that places similar content close together.
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