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This algorithm directly generates association rule without finding frequent patterns.
During the course of mining frequent patterns, the method locates a subset of frequent patterns called locally maximal patterns.
Compact frequent patterns keep exact or approximate supports of a complete set of frequent patterns, and the number of them is often orders of magnitude smaller.
Two sets of map-reduce implementations are used to uncover singleton and non-singleton frequent patterns.
CMTreeMiner [5] mines both closed and maximal frequent patterns from a set of small trees.
The method first computes a set of tiles, which are closed frequent patterns of depth 1.
It uses a single map and reduce phases to get frequent patterns.
Every machine finds frequent patterns for its sample and reducer combines the results.
To generate such association rules, frequent patterns must be identified first.
Then, it develops a hooking strategy that reconstructs the closed frequent patterns from these tiles.
Repeat steps 2 and 3 until no more frequent patterns are generated. .
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