The data warehouse and data mining
Communications of the ACM
Building the Data Warehouse,3rd Edition
Building the Data Warehouse,3rd Edition
In Pursuit of Patterns in Data Reasoning from Data The Rough Set Way
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Effective data mining: a data warehouse-backboned architecture
CASCON '98 Proceedings of the 1998 conference of the Centre for Advanced Studies on Collaborative research
Ad-Hoc Association-Rule Mining within the Data Warehouse
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 8 - Volume 8
Interpretations of Association Rules by Granular Computing
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Rule interestingness analysis using OLAP operations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Enhanced mining of association rules from data cubes
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
Multi-Tier Granule Mining for Representations of Multidimensional Association Rules
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Extended random sets for knowledge discovery in information systems
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
International Journal of Intelligent Information and Database Systems
Building the data warehouse of frequent itemsets in the DWFIST approach
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
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It is a big challenging issue to assure the quality of multidimensional association rules due to the complexity of the association between attributes. Granule mining divides data attributes into multi-tiers and compresses them into granules based on these tiers. Useful rules then can be justified according to the relationship between granules in tiers. Meanwhile, data warehousing is an ideal platform in handling enormous data that helps data mining to focus on representations of rules that best fit users' interests. In this paper, a granule oriented data warehouse model is proposed where the association mappings are implemented to represent the relationship between granules in multi-tiers. Experiments show that the proposed solution achieves encouraging performance.