ERA

Download the full-sized PDF of A Study on Interestingness Measures for Associative ClassifiersDownload the full-sized PDF

Analytics

Share

Permanent link (DOI): https://doi.org/10.7939/R3H35F

Download

Export to: EndNote  |  Zotero  |  Mendeley

Communities

This file is in the following communities:

Graduate Studies and Research, Faculty of

Collections

This file is in the following collections:

Theses and Dissertations

A Study on Interestingness Measures for Associative Classifiers Open Access

Descriptions

Other title
Subject/Keyword
Associative classifier
Interestingness measure
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Jalali Heravi, Mojdeh
Supervisor and department
Zaiane, Osmar R. (Computing Science)
Examining committee member and department
Rafiei, Davood (Computing Science)
Kurgan, Lukasz (Electrical and Computer Engineering)
Department
Department of Computing Science
Specialization

Date accepted
2009-09-18T16:58:26Z
Graduation date
2009-11
Degree
Master of Science
Degree level
Master's
Abstract
Associative classification is a rule-based approach to classify data relying on association rule mining by discovering associations between a set of features and a class label. Support and confidence are the de-facto “interestingness measures” used for discovering relevant association rules. The support-confidence framework has also been used in most, if not all, associative classifiers. Although support and confidence are appropriate measures for building a strong model in many cases, they are still not the ideal measures because in some cases a huge set of rules is generated which could hinder the effectiveness in some cases for which other measures could be better suited. There are many other rule interestingness measures already used in machine learning, data mining and statistics. This work focuses on using 53 different objective measures for associative classification rules. A wide range of UCI datasets are used to study the impact of different “interestingness measures” on different phases of associative classifiers based on the number of rules generated and the accuracy obtained. The results show that there are interestingness measures that can significantly reduce the number of rules for almost all datasets while the accuracy of the model is hardly jeopardized or even improved. However, no single measure can be introduced as an obvious winner.
Language
English
DOI
doi:10.7939/R3H35F
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication

File Details

Date Uploaded
Date Modified
2014-04-30T23:21:04.183+00:00
Audit Status
Audits have not yet been run on this file.
Characterization
File format: pdf (Portable Document Format)
Mime type: application/pdf
File size: 876029
Last modified: 2015:10:12 13:17:10-06:00
Filename: Jalali Heravi_Mojdeh_Fall 2009.pdf
Original checksum: 65d9c21e7e98771ae45b15609b7511b5
Well formed: true
Valid: true
Page count: 142
Activity of users you follow
User Activity Date