Facilitating Active Multidimensional Association Mining with User Preference Ontology

Multidimensional association mining from data warehouse has become a knowledge discovery paradigm because it provides more specific conditional settings for target mining data, thus can generate rules more close to users’ needs. Yet data warehouse is subject to change by time or the modifications of...

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Vydané v:Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 Ročník 3; s. 437 - 440
Hlavní autori: Wu, Chin-Ang, Lin, Wen-Yang, Wu, Chuan-Chun
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: Washington, DC, USA IEEE Computer Society 15.09.2009
IEEE
Edícia:ACM Conferences
Predmet:
Active mining
Association rules
Computing methodologies > Artificial intelligence > Knowledge representation and reasoning
Computing methodologies > Artificial intelligence > Knowledge representation and reasoning > Semantic networks
Computing methodologies > Machine learning > Machine learning approaches > Rule learning
Conferences
Data mining
Data warehouse mining
Data warehouses
Electronic mail
Information management
Information systems > Data management systems > Information integration > Data warehouses
Information systems > Information retrieval > Users and interactive retrieval > Personalization
Information systems > Information systems applications > Data mining
Information systems > Information systems applications > Decision support systems > Data warehouses
Information systems > World Wide Web > Web searching and information discovery > Personalization
Intelligent agent
Knowledge management
Multidimensional association rule mining
Multidimensional systems
Ontologies
Ontology
Multidimensional association rule mining
Ontology
Active mining
Data warehouse mining
ISBN:0769538010, 9780769538013
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Shrnutí:Multidimensional association mining from data warehouse has become a knowledge discovery paradigm because it provides more specific conditional settings for target mining data, thus can generate rules more close to users’ needs. Yet data warehouse is subject to change by time or the modifications of business rules. Users might not know this change and reinitiate mining queries, which elicits the necessity of an active mining mechanism to bring new knowledge to users dynamically. In this paper, we propose an active multidimensional association mining system framework that incorporates with the user preference ontology that exploits frequent and representative queries. With the assistance of the user preference ontology and its association with user profile, the proposed system can facilitate active mining mechanism, allowing distribution of the renewal mining results to the specific users automatically.
ISBN:0769538010
9780769538013
DOI:10.1109/WI-IAT.2009.320