Interesting Rule Induction Module: Adding Support for Unknown Attribute Values

IRIM (Interesting Rule Induction Module) is a rule induction system designed to induce particularly strong, simple rule sets. Additionally, IRIM does not require prior discretization of numerical attribute values. IRIM does not necessarily produce consistent rules that fully describe the target concepts, however, the rules induced by IRIM often lead to novel revelations of hidden relationships in a dataset. In this paper, we attempt to extend the IRIM system to be able to handle missing attribute values (in particular, lost and do-not-care attribute values) more thoroughly than ignoring the cases that they belong to. Further, we include an implementation of IRIM in the modern programming language Python that has been written for easy inclusion in within a Python data mining package or library. The provided implementation makes use of the Pandas module which is built on top of a C back end for quick performance relative to the performance normally found with Python.

Grantor dc:publisher

University of Kansas

Year dc:date.issued

2016

Author dc:creator

• Lindsey, Theodore S.

Advisor dc:contributor.advisor

• Grzymala-Busse, Jerzy W

• Computer science
• Data mining
• Data Science
• IRIM
• Rule Induction

Statement dc:rights

• Copyright held by the author.

Language dc:language.iso

en

Dc Identifier Other

http://dissertations.umi.com/ku:15002

OAI identifier oai:identifier

oai:kuscholarworks.ku.edu:1808/24163