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 quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
High performance multidimensional analysis of large datasets
Proceedings of the 1st ACM international workshop on Data warehousing and OLAP
Cubegrades: Generalizing Association Rules
Data Mining and Knowledge Discovery
Discovery of Multiple-Level Association Rules from 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
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
Mining Constrained Gradients in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Efficient multidimensional data representations based on multiple correspondence analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
M2SP: mining sequential patterns among several dimensions
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
Granule Oriented Data Warehouse Model
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Closed Non Derivable Data Cubes Based on Non Derivable Minimal Generators
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Mining convergent and divergent sequences in multidimensional data
International Journal of Business Intelligence and Data Mining
Expert Systems with Applications: An International Journal
OLAP over continuous domains via density-based hierarchical clustering
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Advanced Mining of Association Rules over Periodic Snapshots in a Data Warehouse
Proceedings of the 13th International Conference on Knowledge Management and Knowledge Technologies
Discovering diverse association rules from multidimensional schema
Expert Systems with Applications: An International Journal
Data guided approach to generate multi-dimensional schema for targeted knowledge discovery
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Discovering descriptive rules in relational dynamic graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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On-line analytical processing (OLAP) provides tools to explore and navigate into data cubes in order to extract interesting information. Nevertheless, OLAP is not capable of explaining relationships that could exist in a data cube. Association rules are one kind of data mining techniques which finds associations among data. In this paper, we propose a framework for mining inter-dimensional association rules from data cubes according to a sum-based aggregate measure more general than simple frequencies provided by the traditional COUNT measure. Our mining process is guided by a meta-rule context driven by analysis objectives and exploits aggregate measures to revisit the definition of support and confidence. We also evaluate the interestingness of mined association rules according to Lift and Loevinger criteria and propose an efficient algorithm for mining inter-dimensional association rules directly from a multidimensional data.