NDLI: Ontology-driven Rule Generalization and Categorization for Market Data

Content Provider	IEEE Xplore Digital Library
Author	Dongwoo Won McLeod, D.
Copyright Year	2007
Description	Author affiliation: Univ. of Southern California, Los Angeles (Dongwoo Won; McLeod, D.)
Abstract	Radio Frequency Identification (RFID) is an emerging technique that can significantly enhance supply chain processes and deliver customer service improvements. RFID provides user with efficient tracking on the flow of products throughout the wholesale process. However, the large number of information that has been generated from such a process creates difficulty in extracting and analyzing useful information. In this paper, we propose a method to mine the large data sets that allows smaller and more relevant search space compared to the original data sets. Our work is constructed from the following approaches: ontology-driven rule generalization which concentrates on controlling the level of items, and rule categorization using hierarchical association rule clustering that group the generated rules from the given problem space into hierarchical search space. The detailed steps for rule generalization based on ontologies sire presented, as well as the algorithms for rule categorization using hierarchical association rule clustering is developed. Our experiment proves the feasibility of our work which shows the significant reduction of the search space by decreasing the number of rules to be looked at and increasing the relevance among the rules.
Starting Page	917
Ending Page	923
File Size	456657
Page Count	7
File Format	PDF
ISBN	9781424408313
DOI	10.1109/ICDEW.2007.4401085
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-17
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ontology Ontologies RFID Association Rules Data Mining Association rules Data mining RFID tags Information analysis Computer science Privacy Space technology Frequency Hierarchical Clustering Radiofrequency identification
Content Type	Text
Resource Type	Article

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