02-assoc

Dont be surprised if you find six packs next to

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Unformatted text preview: if you find six-packs next to diapers! TID Items 1 2 3 4 5 Bread, Coke, Milk Beer, Bread Beer, Coke, Diaper, Milk Beer, Bread, Diaper, Milk Coke, Diaper, Milk 1/5/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer} 4 A large set of items e.g., things sold in a supermarket TID Items 1 2 3 4 5 Bread, Coke, Milk Beer, Bread Beer, Coke, Diaper, Milk Beer, Bread, Diaper, Milk Coke, Diaper, Milk A large set of baskets, each is a small subset of items e.g., the things one customer buys on one day Can be used to model any many-many relationship, not just in the retail setting Find “interesting” connections between items 1/5/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 5 Given a set of baskets Want to discover association rules: People who bought {x,y,z} tend to buy {v,w} Amazon! Input: TID Items 1 2 3 4 5 Bread, Coke, Milk Beer, Bread Beer, Coke, Diaper, Milk Beer, Bread, Diaper, Milk Coke, Diaper, Milk Output: Rules Discovered: 2 step approach {Milk} --> {Coke} {Diaper, Milk} --> {Beer} 1) Find frequent itemsets 2) Generate the association rules 1/5/2011 Jure Leskovec, Stanford C246: Mining Massive Datasets 6 Simplest question: Find sets of items that appear together “frequently” in baskets Support for itemset I: number of baskets containing all items in I Often expressed as a fraction of the total number of baskets Given a support threshold s, then sets of items that appe...
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This document was uploaded on 02/26/2014 for the course CS 246 at Stanford.

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