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In contrast to [[supervised learning]] where models learn to map the input to the target output (e.g. images labeled as a "cat" or "fish"), unsupervised methods learn concise representations of the input data, which can be used for data exploration or to analyze or generate new data. The other levels in the supervision spectrum are [[reinforcement learning]] where the machine is given only a "performance score" as guidance, and [[semi-supervised learning]] where only a portion of training data is labeled.
Some of the most popular unsupervised learning algorithms:
* K-means clustering
* KN(k-nearest neighbors)
* Hierarchal clustering
* Anomaly detection
* Neural Networks
* Principle Component Analysis
* Independent Component Analysis
* Apriori algorithm
* Singular value decomposition<ref>{{Cite web |last=Bhat |first=Harshini |date=12 June 2023 |title=Introduction to Unsupervised Learning Algorithms |url=https://www.almabetter.com/bytes/articles/unsupervised-learning-algorithms |website=almabetter.com}}</ref>
== Neural networks ==
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