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The experimental results indicate that UAMFI is an efficient algorithm for updating Maximal Frequent Itemsets.An itemset-tree is a special data structure that can be used for performing efficient queries about itemsets and association rules in a transaction database without having to generate all of them beforehand.
Finally, the algorithm was tested on the mushroom and T15I4D100K database, and UAMFI's performances were compared with Mafia.The run-time performance of FP-growth depends on the compaction factor of the dataset.If the resulting conditional FP-trees are very bushy (in the worst case, a full prefix tree), then the performance of the algorithm degrades significantly because it has to generate a large number of subproblems and merge the results returned by each subproblem.Step 2 – Next step would be to update the support count of the nodes to only represent those paths which contain node p.For example, contains many paths without node p like , so we have to update the support counts.
From the next blog, we will be diving into how to extract association rules from the extracted frequent items. References – Akshansh Jain is a Software Consultant having more than 1 year of experience.