AI engineering glossary · Classical ML algorithms
What is mini-batch k-means?
Mini-batch k-means: A faster variant of k-means that updates the centres using small random batches of data at each step instead of all the rows. It trades slight accuracy for large speed and memory savings on very big datasets.
Mini-batch k-means: an example
MiniBatchKMeans(batch_size=1024) clusters 20 million app-event rows without loading them all at once.
Related terms
More Classical ML algorithms terms
- Multicollinearity
- Multinomial naive Bayes
- Multiple linear regression
- Naive Bayes
- Non-negative matrix factorisation
- Normal equation
- Odds ratio
- One-class SVM
Where you learn it
Mini-batch k-means is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.