Key Concepts : Frequent Itemsets: The sets of item which has minimum support (denoted by Li for ith-Itemset). Apriori Property: Any subset of frequent itemset must be frequent. Join Operation: To find Lk , a set of candidate k-itemsets is generated by joining Lk-1 with itself. Join Step: Ck is generated by joining Lk-1with itself Step: Any (k-1)-itemset that is not frequent cannot be a subset of a frequent k-itemset Pseudo-code: Candidate itemset of size k Ck: : frequent itemset of size k Lk = {frequent L1 for (k = 1; !=; do begin Lk k++) = candidates generated from Ck+1 Lk; for each transaction in database do t increment the count of all candidates in that are Ck+1 contained in = candidates in with min_support t Lk+1 Ck+1 end return k Lk; 6. Discuss about data mining architecture. Data mining (knowledge discovery in databases): Extraction of interesting (non-trivial, implicit, previously unknown and – potentially useful) information or patterns from data in large databases 7 Data Mining Steps 1. Data cleaning remove noise and inconsistent data –
Key Concepts : Frequent Itemsets: The sets of item which has minimum support (denoted by Li for ith-Itemset). Apriori Property: Any subset of frequent itemset must be frequent. Join Operation: To find Lk , a set of candidate k-itemsets is generated by joining Lk-1 with itself. Join Step: Ck is generated by joining Lk-1with itself Step: Any (k-1)-itemset that is not frequent cannot be a subset of a frequent k-itemset Pseudo-code: Ck: Candidate itemset of size k Lk : frequent itemset of size k L1 = {frequent 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 end return k Lk; 6. Discuss about data mining architecture. Data mining (knowledge discovery in databases): – Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases 7 Data Mining Steps 1. Data cleaning – remove noise and inconsistent data