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.
M: Determines the maximum length of a classification rule (its number of features plus 1, because class attribute is also considered). Mining large rules is costly, but sometimes the accuracy gain is worth. The default value for this option is 4.
S: Determines the support threshold for pruning (default: 0).
C: Determines the confidence threshold for pruning (default: 0).
The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.
Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.
Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, String attributes, Missing values, Nominal class, Binary class] Dependencies:  min # Instance: 1
It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.7) from the below update site following our NodePit Product and Node Installation Guide:
A zipped version of the software site can be downloaded here.
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to firstname.lastname@example.org, follow @NodePit on Twitter, or chat on Gitter!
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.