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These paths can be accessed rapidly using the pointers associated with node p.FP-growth finds all the frequent itemsets ending with a particular suffix by employing a divide-and-conquer strategy to split the problem into smaller subproblems.So, first of all, it will find all the frequent items ending in p, then m, b, a, c, and finally f.Since every transaction is mapped onto a path in the FP-tree, we can derive the frequent itemsets ending with a particular item, say p, by examining only the paths containing node p.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.He is familiar with Java but also has knowledge of various other programming languages such as scala, HTML and C .To do that, we will gather all the paths ending in node p.Now, the thing to remember here is that header table already consists of only frequent items, so p itself is frequent and we can expect itemsets ending with p to be frequent as well. The below figure shows the prefix paths for node p.
Once the paths are updated with new support counts, we will eliminate all those items whose support count is less than the minimum support count, in this case, 3. Support for c is 3, which is equal to the minimum support threshold provided.
Because of the low efficiency of Maximal Frequent Itemsets(MFI) updating methods, the MFI's updating methods were analyzed.
A new algorithm UAMFI based on Full Merged Sorted FP-Tree (FMSFP-Tree) was proposed.
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.