Apriori (3.7)

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.


Apriori 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)

Z: Treat zero (i.e. first value of nominal attributes) as missing

B: If used, two characters to use as rule delimiters in the result of toString: the first to delimit fields, the second to delimit items within fields. (default = traditional toString result)

c: The class index. (default = last)

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

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Input Ports

Training data

Output Ports

This node has no output ports

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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.


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