Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Fast sequential and parallel algorithms for association rule mining: a comparison
Fast sequential and parallel algorithms for association rule mining: a comparison
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Mining fuzzy association rules in databases
ACM SIGMOD Record
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A heuristic for mining association rules in polynomial time
Mathematical and Computer Modelling: An International Journal
Discovering a cover set of ARsi with hierarchy from quantitative databases
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Dealing with uncertainty in data mining and information extraction
On a confidence gain measure for association rule discovery and scoring
The VLDB Journal — The International Journal on Very Large Data Bases
A robust associative watermarking technique based on similarity diagrams
Pattern Recognition
Dynamic knowledge validation and verification for CBR teledermatology system
Artificial Intelligence in Medicine
A new approach to classification based on association rule mining
Decision Support Systems
Mining pure linguistic associations from numerical data
International Journal of Approximate Reasoning
An integrated method for finding key suppliers in SCM
Expert Systems with Applications: An International Journal
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Elicitation of fuzzy association rules from positive and negative examples
Fuzzy Sets and Systems
Discovering a cover set of ARsi with hierarchy from quantitative databases
Information Sciences: an International Journal
Distributed data mining for e-business
Information Technology and Management
Watermarking with association rules alignment
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Mining association rules from time series to explain failures in a hot-dip galvanizing steel line
Computers and Industrial Engineering
Engineering Applications of Artificial Intelligence
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Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a problem of concern, as conventional mining algorithms often produce too many rules for decision makers to digest. Instead, this paper concentrates on a smaller set of rules, namely, a set of simple association rules each with its consequent containing only a single attribute. Such a rule set can be used to derive all other association rules, meaning that the original rule set based on conventional algorithms can be 'recovered' from the simple rules without any information loss. The number of simple rules is much less than the number of all rules. Moreover, corresponding algorithms are developed such that certain forms of rules (e.g. 'P⇒?' or '?⇒Q') can be generated in a more efficient manner based on simple rules.