Facilitating Active Multidimensional Association Mining with User Preference Ontology

Authors:
Chin-Ang Wu;Wen-Yang Lin;Chuan-Chun Wu
Affiliations:
-;-;-
Venue:
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Year:
2009

Citing 4
Cited 0

The KDD process for extracting useful knowledge from volumes of data

Communications of the ACM
A framework for measuring changes in data characteristics

PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Active database systems

ACM Computing Surveys (CSUR)
Building the Data Warehouse

Building the Data Warehouse

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.