MIS372 Lecture Notes - Lecture 7: Association Rule Learning, Subsequence, Stock Market
1 Oct 2018
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Document Summary
Lecture 7: association rule mining and sequential pattern analysis. It is a measure of how strongly two (or more) items co-occur: find patterns in the data rather than predicting anything, they are rules extracted from large amounts of data. If a is in the item set, then b will most likely be there too. {items a and items b} -> {items c and item d and item e}: If a shopper buys milk, then they will most likely buy bread too. If a football team gets a penalty, then they will most likely score a goal. If a customer buys one product per quarter, then they will most likely not churn for a year. Baskets of occurrence (e. g. one transaction, one episode of care, one online session) Windows of item (e. g. one day, one quarter [of a game]: data may need to be pre-processed to: It is the relative frequency of occurrence of an item set in the container set.
