The spectral centroid is a measure used in digital signal processing to characterise a spectrum.

In mathematics, (also known as the Guldinus theorem, Pappus–Guldinus theorem or Pappus's theorem) either of two related theorems dealing with the surface areas and volumes of surfaces and solids of revolution.

In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.