For example an entropy-weighted k-means algorithm performed better with sparse data than its standard k-means counterpart. It does this by weighting different variables to ensure that the most ...

A machine-learning approach developed for sparse data reliably predicts fault slip in laboratory earthquakes and could be key to predicting fault slip and potentially earthquakes in the field.

New twist on AI makes the most of sparse sensor data. ScienceDaily. Retrieved June 2, 2025 from www.sciencedaily.com / releases / 2023 / 11 / 231114143734.htm. DOE/Los Alamos National Laboratory.

This is where recommendation algorithms struggle most: they have to draw the most accurate conclusions possible based on sparse data. For example, to give User 1 a recommendation, you could try to ...