Data Mining basics: Find the relevant project-level algorithm in the data, and mine data-level data.

Data Mining basics: Find the relevant project-level algorithm in the data, and mine data-level data.

Ck: Candidate itemset of size kLk: frequent itemset of size kL1 = {frequent items}; for (k = 1; Lk! =; K ++) do begin Ck + 1 = candidates generated from Lk; for each transaction t in database do increment the count of all candidates in Ck + 1 that are contained in t Lk + 1 = candidates in Ck + 1 with min_support endreturn k Lk;

SQL application

Suppose the items in Lk-1 are listed in an orderStep 1: self-joining Lk-1 insert into Ckselect p. item1, p. item2 ,..., P. itemk-1, q. itemk-1from Lk-1 p, Lk-1 qwhere p. item1 = q. item1 ,..., P. itemk-2 = q. itemk-2, p. itemk-1. itemk-1Step 2: pruningforall itemsets c in Ck doforall (k-1)-subsets s of c doif (s is not in Lk-1) then delete c from Ck