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AN EFFICIENT APPROACH TO CATEGORIZING ASSOCIATION
RULES
by
Dongwoo Won
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
August 2010
Copyright 2010 Dongwoo Won
Object Description
| Title | An efficient approach to categorizing association rules |
| Author | Won, Dongwoo |
| Author email | dwon@usc.edu; dongwoo.won@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2010-06-02 |
| Date submitted | 2010 |
| Restricted until | Unrestricted |
| Date published | 2010-07-23 |
| Advisor (committee chair) | McLeod, Dennis |
| Advisor (committee member) |
Nakano, Aiichiro Pryor, Lawrence |
| Abstract | The application of association rules, which specify relationships among large sets of items, is a fundamental data mining technique used for various applications. In this dissertation, we present an efficient method of using association rules for identifying rules from a stream of transactions consisting of a collection of items purchased, referred to as market basket data. A common problem encountered with market basket analysis is that it results in a number of weakly associated rules that are of little interest to the user. To mitigate this problem, we propose an efficient approach to managing the data so that only a reasonable number of rules need to be analyzed. First, we apply an ontology, a hierarchical structure that defines the relationships among concepts at different abstraction levels, to minimize the search space, thereby allowing the user to avoid having to search the large original result set for useful and important rules. Next, we apply a novel metric called relevance to categorize the rules using the Hierarchical Association Rule Categorization (HARC) algorithm, an algorithm that efficiently categorizes association rules by searching the compact generalized rules first and then the specific rules that belong to them, rather than scanning the entire list of rules. The efficiency and effectiveness of our approach is demonstrated in our experiments on high-dimensional synthetic data sets. |
| Keyword | association; clustering; data mining; relevance |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m3211 |
| Rights | Won, Dongwoo |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Won-3913 |
| Archival file | uscthesesreloadpub_Volume35/etd-Won-3913.pdf |
Description
| Title | Page 1 |
| Full text | AN EFFICIENT APPROACH TO CATEGORIZING ASSOCIATION RULES by Dongwoo Won A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) August 2010 Copyright 2010 Dongwoo Won |
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