Page 83 - 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%u062983Benefits of the FP-tree Structure: Completeness: FP tree preserve complete information for frequent pattern mining and never break a long pattern of any transaction. Compactness: FP tree reduce irrelevant information and infrequent items are gone , items in frequency descending order( the more frequently occurring, the more likely to be shared) , never be larger than the original database (not count node-links and the count field). The Frequent Pattern Growth Mining Method: The idea is : Recursively grow frequent patterns by pattern and database partition. We implement it as : For each frequent item, construct its conditional pattern base, and then its conditional FP-tree , repeat the process on each newly created conditional FP-tree . until the resulting FP-tree is empty, or it contains only one path .single path will generate all the combinations of its sub-paths, each of which is a frequent pattern. Advantages of FP-Growth a- Divide-and-conquer: Decompose both the mining task and DB according to the frequent patterns obtained so far , it leads to focused search of smaller database b-Other factors as : no candidate generation and no candidate test , compressed database (FP-tree structure) , no repeated scan of entire database , basic ops: counting local frequent items and building sub FPtree as no pattern search and matching . III. Pattern Evaluation Methods