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
Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Communications of the ACM
ACM Transactions on Information Systems (TOIS)
Data Mining with optimized two-dimensional association rules
ACM Transactions on Database Systems (TODS)
The "DGX" distribution for mining massive, skewed data
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Data-driven evolution of data mining algorithms
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The Data Warehouse Lifecycle Toolkit: Expert Methods for Designing, Developing and Deploying Data Warehouses with CD Rom
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Mining sequential patterns with constraints in large databases
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Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Techniques for Mining Causal Structures
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Mining Frequent Itemsets Using Support Constraints
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Intelligent Rollups in Multidimensional OLAP Data
Proceedings of the 27th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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
Features to consider in a data warehousing system
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Journal of Management Information Systems - Special section: Data mining
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A Query-Driven Approach to the Design and Management of Flexible Database Systems
Journal of Management Information Systems
Expert Systems with Applications: An International Journal
Customer pattern search for after-sales service in manufacturing
Expert Systems with Applications: An International Journal
Discovery of unapparent association rules based on extracted probability
Decision Support Systems
Interpreting the web-mining results by cognitive map and association rule approach
Information Processing and Management: an International Journal
Designing of dynamic labor inspection system for construction industry
Expert Systems with Applications: An International Journal
Resource behavior measure and application in business process management
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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Data warehouses store data that explicitly and implicitly reflect customer patterns and trends, financial and business practices, strategies, know-how, and other valuable managerial information. In this paper, we suggest a novel way of acquiring more knowledge from corporate data warehouses. Association-rule mining, which captures co-occurrence patterns within data, has attracted considerable efforts from data warehousing researchers and practitioners alike. In this paper, we present a new data-mining method called qualified association rules. Qualified association rules capture correlations across the entire data warehouse, not just over an extracted and transformed portion of the data that is required when a standard data-mining tool is used.