LAC (3.7)

Implements the LAC (Lazy Associative Classifier) algorithm, which uses associative rules to execute classifications

Unlike other Apriori-based classifiers, LAC algorithm computes association rules in a demand-driven basis.For each instance to be classified, it filters the training set and produces only useful rules for that instance, outperforming traditional associative classifiers in both time and accuracy.

For more information: [Adriano Veloso, Wagner Meira Jr., Mohammed Zaki.Lazy Associative Classification.

ICDM '06 Proceedings of the Sixth International Conference on Data Mining, Pages 645-654, IEEE Computer Society Washington, DC, USA].

(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: Table
   Training data
   Training data

Output ports

1. Type: Weka 3.7 Classifier
   Trained model
   Trained model