Modified Anti-Monotone Support Pruning on FP Tree for Improved Frequent Pattern Generation

Pattern discovery plays a very important role in mining interesting frequent patterns from any data sources. Besides, it also includes determining whether the pattern is interesting. The study applied the modified anti-monotone support constraint to the Frequent Pattern-Growth (FP-Growth) algorithm to enhance its frequent pattern generation. Besides, the study applied the alteration after constructing the FP tree, thereby traversing and searching the nodes of the tree for possible nodes not satisfying the minimum support. The mining process resulted to a comparable difference in the number of generated itemsets and association rules. The second pruning showed more interesting frequent patterns compared to those generated only from the first pruning. The study used the confidence measure in assessing the algorithm's performance. The result of the evaluation showed that of the two itemsets used in the testing, only very few frequent patterns generated after the first pruning obtained most interesting measures, having 1 out of 6 for itemset S40 and 5 out of 20 for itemset S82. On the other hand, all the frequent patterns generated after the second pruning obtained the most interesting measures. Hence, the dual pruning removes infrequent and weak patterns, leaving only those interesting and strong ones.

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