Association Rule Learning


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Association Rule Learning

discovering rules for observing A given B. This is closely related to clustering in that we’re attempting to find connections between events. However, the difference is the approach. Instead of drawing bounds on a region and seeing if someone fits in that bucket, we use the frequency of a collection of discrete observations to create priors: what’s the probability of observing A, or B, or C? From these, we figure out what the probability is of observing A given B, or observing C given A and B. This is just Bayesian statistics: P(A), P(B | A), P(C | A, B), and so forth.


Machine Learning for Beginners – a How-to Guide https://opendatascience.com/machine-learning-for-beginners/