Pattern-growth is one of several influential frequent pattern mining methodologies, where a pattern (e.g., an itemset, a subsequence, a subtree, or a substructure) is frequent if its occurrence frequency in a database is no less than a specified minimum_support threshold. The (frequent) pattern-growth method mines the data set in a divide-and-conquer way: It first derives the set of size-1 frequent patterns, and for each pattern p, it derives p’s projected (or conditional) database by data set partitioning and mines the projected database recursively. Since the data set is decomposed progressively into a set of much smaller, pattern-related projected data sets, the pattern-growth method effectively reduces the search space and leads to high efficiency and scalability.
Frequent itemset mining was first introduced as an essential subtask of association rule mining by Agrawal et al. . A candidate set generation-and-test approach, represented by the...
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