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The Apriori algorithm to find sets of large elements and generate association rules using those large sets of elements.
Apriori is a program to find association rules and sets of frequent items (also closed and maximal as well as generators) with the Apriori algorithm [Agrawal and Srikant 1994], which performs an initial amplitude search in the subset's lattice and determines the support Of the Assemblies by subset tests element. This implementation is fairly fast since it uses a prefix tree to organize counters for element sets. However, Apriori excels at basically all datasets by depth-early algorithms like Eclat or FP-growth.
This program is currently useful only because it can generate association rules directly (while all other programs available on this website only find frequent article sets) and allows evaluating association rules as well as article sets by a large range of Measures.
The special version of the "apriacc" program is designed for the purpose of finding neural assemblies in peak train data and can be seen as an analog of the implementation of the accretion algorithm since it provides basically the same set of options. However, the output is not identical, because the accumulation performs an incomplete search.