FPGrowth (3.7)

Class implementing the FP-growth algorithm for finding large item sets without candidate generation

Iteratively reduces the minimum support until it finds the required number of rules with the given minimum metric.For more information see:

J.

Han, J.Pei, Y.Yin: Mining frequent patterns without candidate generation.

In: Proceedings of the 2000 ACM-SIGMID International Conference on Management of Data, 1-12, 2000.

(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.

Options

FPGrowth Options

P: Set the index of the attribute value to consider as 'positive' for binary attributes in normal dense instances. Index 2 is always used for sparse instances. (default = 2)

I: The maximum number of items to include in large items sets (and rules). (default = -1, i.e. no limit.)

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

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

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

U: Upper bound for minimum support as a fraction or number of instances. (default = 1.0)

M: The lower bound for the minimum support as a fraction or number of instances. (default = 0.1)

D: The delta by which the minimum support is decreased in each iteration as a fraction or number of instances. (default = 0.05)

S: Find all rules that meet the lower bound on minimum support and the minimum metric constraint. Turning this mode on will disable the iterative support reduction procedure to find the specified number of rules.

transactions: Only consider transactions that contain these items (default = no restriction)

rules: Only print rules that contain these items. (default = no restriction)

use-or: Use OR instead of AND for must contain list(s). Use in conjunction with -transactions and/or -rules

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: [Binary attributes, Unary attributes, Empty nominal attributes, Missing values, No class] 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

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Training data

Output Ports

This node has no output ports

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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.

Workflows

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Links

Developers

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Installation

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:

v5.2

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

Plugin provider: KNIME AG, Zurich, Switzerland
Plugin version: 5.2.0.v202310301556
On NodePit since: 2023-12-06
Last update: 2024-04-15
KNIME versions: Since v3.6

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