Apriori (3.7)

Learner

Class implementing an Apriori-type algorithm

Iteratively reduces the minimum support until it finds the required number of rules with the given minimum confidence.The algorithm has an option to mine class association rules.

It is adapted as explained in the second reference.

For more information see:

R.Agrawal, R.

Srikant: Fast Algorithms for Mining Association Rules in Large Databases.In: 20th International Conference on Very Large Data Bases, 478-499, 1994.

Bing Liu, Wynne Hsu, Yiming Ma: Integrating Classification and Association Rule Mining.

In: Fourth International Conference on Knowledge Discovery and Data Mining, 80-86, 1998.

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.

Input Ports

  1. Type: Data
    Training data

Extension

This node is part of the extension

KNIME Weka Data Mining Integration (3.7)

v4.0.0

Short Link

Drag node into KNIME Analytics Platform