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This algorithm works well in the classical association rule mining setting where the data set is typically sparse, the support threshold is set sufficiently high to ensure that there are only a manageable number of frequent sets, and the goal is to mine all of the frequent sets in order to discover interesting association rules.
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However, compared to the GGGGCC repeat, which has an optimal organization to maximize base pairing, the CCCCGG repeat has fewer base pairs and more frequent sets of four unpaired C nucleotides within the stem, and thus relies more on base stacking effects.
The identified maximally frequent sets are added to the set of seed gene sets B and the genes in B are deleted from the binary matrix E u (E d ).
A system of frequent sets is a collection of such pairs.
To learn action models, ARMS gathers knowledge on the statistical distribution of frequent sets of actions in the example plans.
Every system of frequent sets has a unique completion that actually represents all knowledge that can be derived.
In addition, it requires repeated scanning of the entire dataset until all possible combinations of frequent sets are found.
This is why it builds size k frequent sets by combining size k-1 frequent sets that share k-2 items.
To find the frequent sets we count the support of each candidate set.
Also the data sets tend to be much denser than transaction databases, which increases further the number of frequent sets.
The MAFIA algorithm is an efficient implementation for finding maximally frequent sets with support above a given threshold [ 28].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com