FPGrowth (3.6)


Class implementing the FP-growth algorithm for finding large item sets without candidate generation. Iteratively reduces the minimum support until it finds the required number of rules with the given minimum metric. For more information see: J. Han, J.Pei, Y. Yin: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM-SIGMID International Conference on Management of Data, 1-12, 2000.

(based on WEKA 3.6)

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Input Ports

  1. Type: Data
    Training data


This node is part of the extension

KNIME Weka Data Mining Integration (3.6)


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