Description

Frequent Item Mining What is data mining? =Pattern Mining? What patterns? Why are they useful? Definition: Frequent Itemset Itemset A collection of one or more items Example: {Milk, Bread, Diaper} k-itemset An itemset that contains k items Support count () Frequency of occurrence of an itemset E.g. ({Milk, Bread,Diaper}) = 2 TID Items 1 Bread, Milk 2 3 4 5 Bread, Diaper, Beer, Eggs Milk, Diaper, Beer, Coke Bread, Milk, Diaper, Beer Bread, Milk, Diaper, Coke Support Fraction of transactions that contain an itemset E.g. s({Milk, Bread, Diaper}) = 2/5 Frequent Itemset An itemset whose support is greater than or equal to a minsup threshold 3 Frequent Itemsets Mining TID Transactions 100 { A, B, E } Minimum support level 50% 200 { B, D } {A},{B},{C},{A,B}, {A,C} 300 { A, B, E } 400 { A, C } 500 { B, C } 600 { A, C } 700 { A, B } 800 { A, B, C, E } 900 { A, B, C } 1000 { A, C, E } How to link this to Data Cube? Three Different Views of FIM Transactional Database How we do store a transactional database? Horizontal, Vertical, Transaction-Item Pair TID Items 1 Bread, Milk 2 3 4 5 Bread, Diaper, Beer, Eggs Milk, Diaper, Beer, Coke Bread, Milk, Diaper, Beer Bread, Milk, Diaper, Coke Binary Matrix Bipartite Graph How does the FIM formulated in these different settings? 5 Frequent Itemset Generation null A B C D E AB AC AD AE BC BD BE CD CE DE ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE ABCD ABCE ABDE ABCDE ACDE BCDE Given d items, there are 2d possible candidate itemsets 6 Frequent Itemset Generation Brute-force approach: Each itemset in the lattice is a candidate frequent itemset Count the support of each candidate by scanning the database Transactions N TID 1 2 3 4 5 Items Bread, Milk Bread, Diaper, Beer, Eggs Milk, Diaper, Beer, Coke Bread, Milk, Diaper, Beer Bread, Milk, Diaper, Coke List of Candidates M w Match each transactio

AI summary