unsupervised clustering

Terms from Artificial Intelligence: humans at the heart of algorithms

The glossary is being gradually proof checked, but currently has many typos and misspellings.

Most clustering algorithms are unsupervised, simply, taking groups of values and associating items based on similarity of attributes. Of cousre, the idea of what similarity means for a particular dataset does have to be specified for the algorithm used, or is a fixed feature of the algorithm such as a geeral notion of a compact cluster.
Examples of unsupervised clustering algorithms include k-means and self-organising maps.

Used in Chap. 18: page 286