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dc.contributor.advisorGrzymala-Busse, Jerzy W
dc.contributor.authorLindsey, Theodore S.
dc.date.accessioned2017-05-15T01:40:52Z
dc.date.available2017-05-15T01:40:52Z
dc.date.issued2016-12-31
dc.date.submitted2016
dc.identifier.otherhttp://dissertations.umi.com/ku:15002
dc.identifier.urihttp://hdl.handle.net/1808/24163
dc.description.abstractIRIM (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.
dc.format.extent49 pages
dc.language.isoen
dc.publisherUniversity of Kansas
dc.rightsCopyright held by the author.
dc.subjectComputer science
dc.subjectData mining
dc.subjectData Science
dc.subjectIRIM
dc.subjectRule Induction
dc.titleInteresting Rule Induction Module: Adding Support for Unknown Attribute Values
dc.typeThesis
dc.contributor.cmtememberLuo, Bo
dc.contributor.cmtememberKulkarni, Prasad
dc.thesis.degreeDisciplineElectrical Engineering & Computer Science
dc.thesis.degreeLevelM.S.
dc.identifier.orcid
dc.rights.accessrightsopenAccess


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