Page 74 - Demo

%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u062974{a1, %u2026, a50}: 2 What is the set of max-pattern? {a1, %u2026, a100}: 1 What is the set of all patterns? {a1}: 2, %u2026, {a1, a2}: 2, %u2026, {a1, a51}: 1, %u2026, {a1, a2, %u2026, a100}: 1 We find two closed frequent itemsets and their support counts, that is, C = {{a1, a2,..., a100} : 1; {a1, a2,..., a50} : 2}. There is only one maximal frequent itemset: M = {{a1, a2,..., a100} : 1}. Notice that we cannot include {a1, a2,..., a50} as a maximal frequent itemset because it has a frequent superset, {a1, a2,..., a100}. Compare this to the preceding where we determined that there are 2 100 %u2212 1 frequent itemsets, which are too many to be enumerated! The set of closed frequent itemsets contains complete information regarding the frequent itemsets. For example, from C, we can derive, say, (1) {a2, a45 : 2} since {a2, a45} is a sub-itemset of the itemset {a1, a2,..., a50 : 2}; and (2) {a8, a55 : 1} since {a8, a55} is not ,a sub-itemset of the previous itemset but of the itemset {a1, a2,..., a100 : 1}. However, from the maximal frequent itemset, we can only assert that both itemsets ({a2, a45} and {a8, a55}) are frequent, but we cannot assert their actual support counts. I. Frequent Itemset Mining Methods 1- Apriori Algorithm (A Candidate Generation-and-Test Approach): Before talking about the apriori algorithm we must consider The downward closure property of frequent patterns which says that Any subset of a frequent itemset must be frequent ,If {beer, diaper, nuts} is frequent, so is {beer, diaper} so every transaction having {beer, diaper, nuts} also contains {beer, diaper} . Apriori is a seminal algorithm for mining frequent itemsets for Boolean association rules, Apriori employs an iterative approach known as a level-wise search, where k-itemsets are used to explore (k + 1)-itemsets. First, the set of frequent 1-itemsets is found by scanning the database to