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
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Data mining
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 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
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Exploratory mining via constrained frequent set queries
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
DMajor—Application Programming Interface for Database Mining
Data Mining and Knowledge Discovery
ICSC '99 Proceedings of the 5th International Computer Science Conference on Internet Applications
A New Algorithm for Faster Mining of Generalized Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Mining Association Rules: Deriving a Superior Algorithm by Analyzing Today's Approaches
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
From concepts to clinical reality: an essay on the benchmarking of biomedical terminologies
Journal of Biomedical Informatics - Special issue: Biomedical ontologies
Evaluating generalized association rules through objective measures
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Statistical mining of interesting association rules
Statistics and Computing
Standardising the lift of an association rule
Computational Statistics & Data Analysis
Data mining on multimedia data
Data mining on multimedia data
Information Systems Frontiers
Implementing an efficient causal learning mechanism in a cognitive tutoring agent
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
A cognitive tutoring agent with episodic and causal learning capabilities
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
A computational model for causal learning in cognitive agents
Knowledge-Based Systems
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In this paper we deal with association rule mining in the context of a complex, interactive and iterative knowledge discovery process. After a general introduction covering the basics of association rule mining and of the knowledge discovery process in databases we draw the attention to the problematic aspects concerning the integration of both. Actually, we come to the conclusion that with regard to human involvement and interactivity the current situation is far from being satisfying. In our paper we tackle this problem on three sides: First of all there is the algorithmic complexity. Although today's algorithms efficiently prune the immense search space the achieved run times do not allow true interactivity. Nevertheless we present a rule caching schema that significantly reduces the number of mining runs. This schema helps to gain interactivity even in the presence of extreme run times of the mining algorithms. Second, today the mining data is typically stored in a relational database management system. We present an efficient integration with modern database systems which is one of the key factors in practical mining applications. Third, interesting rules must be picked from the set of generated rules. This might be quite costly because the generated rule sets normally are quite large whereas the percentage of useful rules is typically only a very small fraction. We enhance the traditional association rule mining framework in order to cope with this situation.