Page 79 - 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%u062979is a hash k-itemsets into buckets and a k-itemset whose bucket count is below the threshold cannot be frequent, this method is useful for 2-itemsets , it works as first generating a hash table of 2-itemsets during the scan for 1-itemset then If the count of a bucket is below minimum support count, the itemsets in the bucket should not be included in candidate 2 itemsets. c- Sampling for Frequent Patterns : in this method , we select a sample of original database, mine frequent patterns within sample using Apriori, then Scan database once to verify frequent itemsets found in sample, in this step only borders of closure of frequent patterns are checked.E.g, check abcd instead of ab, ac, %u2026 then the last step is scanning database again to find missed frequent patterns. d- DIC: Reduce Number of Scans: Once both A and D are determined frequent, the counting of AD begins,once all length-2 subsets of BCD are determined frequent, the counting of BCD begins. 3- FPGrowth (A Frequent Pattern-Growth Approach): Is an approach to overcome bottlenecks in the apriori method as in the apriori method we use the candidate generation and test that often