Java-friendly version of run
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Java-friendly version of run
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Computes an FP-Growth model that contains frequent itemsets.
Computes an FP-Growth model that contains frequent itemsets.
input data set, each element contains a transaction
Sets the minimal support level (default: 0.3
).
Sets the minimal support level (default: 0.3
).
Sets the number of partitions used by parallel FP-growth (default: same as input data).
Sets the number of partitions used by parallel FP-growth (default: same as input data).
A parallel FP-growth algorithm to mine frequent itemsets. The algorithm is described in Li et al., PFP: Parallel FP-Growth for Query Recommendation. PFP distributes computation in such a way that each worker executes an independent group of mining tasks. The FP-Growth algorithm is described in Han et al., Mining frequent patterns without candidate generation.
Association rule learning (Wikipedia)