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Apriori (3.6)

KNIME WEKA nodes version 2.10.2.v202012020943 by KNIME AG, Zurich, Switzerland

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 3.6)

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

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

Options

Class column
Choose the column that contains the target variable.
Preliminary Attribute Check

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, Missing values, No class, Nominal class, Binary class, Missing class values] Dependencies: [] min # Instance: 1

Associator Options

N: The required number of rules. (default = 10)

T: The metric type by which to rank rules. (default = confidence)

C: The minimum confidence of a rule. (default = 0.9)

D: The delta by which the minimum support is decreased in each iteration. (default = 0.05)

U: Upper bound for minimum support. (default = 1.0)

M: The lower bound for the minimum support. (default = 0.1)

S: If used, rules are tested for significance at the given level. Slower. (default = no significance testing)

I: If set the itemsets found are also output. (default = no)

R: Remove columns that contain all missing values (default = no)

V: Report progress iteratively. (default = no)

A: If set class association rules are mined. (default = no)

c: The class index. (default = last)

Input Ports

Icon
Training data

Views

Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.

Installation

To use this node in KNIME, install KNIME Weka Data Mining Integration (3.6) from the following update site:

KNIME 4.3

A zipped version of the software site can be downloaded here.

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Developers

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