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Mastering Data Mining with Python - Find patterns hidden in your data

You're reading from  Mastering Data Mining with Python - Find patterns hidden in your data

Product type Book
Published in Aug 2016
Publisher
ISBN-13 9781785889950
Pages 268 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Megan Squire Megan Squire
Profile icon Megan Squire

Towards association rules


All of this frequent itemset stuff is fine, but we are ultimately on the hunt for association rules, which are much more exciting. Association rules are formed from frequent itemsets, with a few small twists. We are interested in making a statement about the frequent itemsets like this: people who buy vanilla wafers also buy bananas 60% of the time. In order to do so, we need to learn how to calculate a few additional metrics, starting with two we call support and confidence.

Support

If we are looking for frequent itemsets, then we also need a way to express how often we see these sets occurring in baskets, and whether that number qualifies as frequent. If I see {vanilla wafers, bananas} in 90% of baskets, is that considered frequent? What about 50% of baskets? What about 5%? We call this number the support of the itemset. The support is just the number of times we saw that itemset over all the baskets.

To make support more meaningful, and to begin talking about "interestingness...

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