Interesting Rule Induction Module: Adding Support for Unknown Attribute Values
Issue Date
2016-12-31Author
Lindsey, Theodore S.
Publisher
University of Kansas
Format
49 pages
Type
Thesis
Degree Level
M.S.
Discipline
Electrical Engineering & Computer Science
Rights
Copyright held by the author.
Metadata
Show full item recordAbstract
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.
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- Engineering Dissertations and Theses [1055]
- Theses [3906]
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