Enhanced mining of association rules from data cubes
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
Comprehensive data warehouse exploration with qualified association-rule mining
Decision Support Systems
Granule Oriented Data Warehouse Model
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Mining convergent and divergent sequences in multidimensional data
International Journal of Business Intelligence and Data Mining
Embedded indicators to facilitate the exploration of a data cube
International Journal of Business Intelligence and Data Mining
International Journal of Intelligent Information and Database Systems
Extending the UML for designing association rule mining models for data warehouses
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Distributed architecture for association rule mining
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
Discovering diverse association rules from multidimensional schema
Expert Systems with Applications: An International Journal
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Many organizations often underutilize their already constructed data warehouses. In this paper, we suggest a novel way of acquiring more information from corporate data warehouses without the complications and drawbacks of deploying additional software systems. Association-rule mining, which captures co-occurrence patterns within data, has attracted considerable efforts from data warehousing researchers and practitioners alike. Unfortunately, most data mining tools are loosely coupled, at best, with the data warehouse repository.Furthermore, these tools can often find association rules only within the main fact table of the data warehouse (thus ignoring the information-rich dimensions of the star schema) and are not easily applied on non-transaction level data often found in data warehouses. In this paper, we present a new data-mining framework that is tightly integrated with the data warehousing technology. Our framework has several advantages over the use of separate data mining tools. First, the data stays at the data warehouse, and thus the management of security andprivacy issues is greatly reduced. Second, we utilize the query processing power of a data warehouse itself, without using a separate data-mining tool. In addition, this framework allows ad-hoc data mining queries over the whole data warehouse, not just over a transformedportion of the data that is required when a standard data-mining tool is used. Finally, this framework also expands the domain of association-rule mining from transaction-level data to aggregated data as well.