STARK association rule learner version 0.1.200.v201906231629 by Universidad de Cantabria, Spain
Self-tuning miner of filtered association rules.
Computes closure-based association rules using confidence boost as a criterion to suppress intuitively redundant rules. This node is intended to be usable with no parameter tuning, although it does support some advanced configuration.
Due to inherent complexities of computation, this node may take several minutes to complete. Be patient.
For dense datasets, you might need to increase the available memory for the JVM; to do this, tweak the -Xmx parameter in file knime.ini (read further on in the KNIME documentation if necessary).
The best way to use this node is along with a File Reader node to read the transactions as strings: set delimiter (often a space) and check "allow short lines" permission in the "advanced" area. Connect it to a Create Collection Column node - preferably with "set" option checked in case items might be repeated on transactions. "Missing values" are automatically ignored. The resulting output is a valid input for this node.
The first output port of the node contains rules given as antecedent and consequent collections along with its details. The second output has gone through some string formatting and is appropriate to be written to a file (e.g. via a CSV Writer node).
This node has been implemented in cooperation with Universidad de Cantabria and Universitat Politecnica de Catalunya with partial support from Pascal 2 through the STARK project of the Harvest Programme.
To use this node in KNIME, install Self-Tuning Association Rules for KNIME from the following update site:
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.
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 email@example.com, 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.