Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining knowledge at multiple concept levels
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Building the data warehouse (2nd ed.)
Building the data warehouse (2nd ed.)
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Modelling Large Scale OLAP Scenarios
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining Flexible Multiple-Level Association Rules in All Concept Hierarchies (Extended Abstract)
DEXA '98 Proceedings of the 9th International Conference on Database and Expert Systems Applications
Improving query response time in scientific databases using data aggregation -a case study
DEXA '96 Proceedings of the 7th International Workshop on Database and Expert Systems Applications
Conceptual modeling for classification mining in data warehouses
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
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
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Data Mining and Data Warehousing are two hot topics in the database research area. Until recently, conventional data mining algorithms were primarily developed for a relational environment. But a data warehouse database is based on a multidimensional model. In our paper we apply this basis for a seamless integration of data mining in the multidimensional model for the example of discovering association rules. Furthermore, we propose this method as a userguided technique because of the clear structure both of model and data. We present both the theoretical basis and efficient algorithms for data mining in the multidimensional data model. Our approach uses directly the requirements of dimensions, classifications and sparsity of the cube. Additionally we give heuristics for optimizing the search for rules.