Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th 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
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
T-Trees, Vertical Partitioning and Distributed Association Rule Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Partitioning strategies for distributed association rule mining
The Knowledge Engineering Review
Computing frequent itemsets in parallel using partial support trees
Journal of Systems and Software
The effect of threshold values on association rule based classification accuracy
Data & Knowledge Engineering
Mining product maps for new product development
Expert Systems with Applications: An International Journal
Integrating data and text mining processes for digital library applications
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
A method for mining quantitative association rules
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
PADUA Protocol: Strategies and Tactics
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
International Journal of Business Intelligence and Data Mining
Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework
New Frontiers in Applied Data Mining
A Framework for Mining Fuzzy Association Rules from Composite Items
New Frontiers in Applied Data Mining
Improved methods for extracting frequent itemsets from interim-support trees
Software—Practice & Experience
Argument Based Moderation of Benefit Assessment
Proceedings of the 2008 conference on Legal Knowledge and Information Systems: JURIX 2008: The Twenty-First Annual Conference
Arguments from Experience: The PADUA Protocol
Proceedings of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008
A Generic and Extendible Multi-Agent Data Mining Framework
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Class noise detection using frequent itemsets
Intelligent Data Analysis
Arguing from Experience to Classifying Noisy Data
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
An adaptive calendar assistant using pattern mining for user preference modelling
Proceedings of the 15th international conference on Intelligent user interfaces
PADUA: a protocol for argumentation dialogue using association rules
Artificial Intelligence and Law
Finding temporal patterns in noisy longitudinal data: a study in diabetic retinopathy
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach
Journal of Intelligent Information Systems
RM-Tool: A framework for discovering and evaluating association rules
Advances in Engineering Software
Antibody-Specified B-Cell Epitope Prediction in Line with the Principle of Context-Awareness
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computing frequent itemsets in parallel using partial support trees
PVM/MPI'05 Proceedings of the 12th European PVM/MPI users' group conference on Recent Advances in Parallel Virtual Machine and Message Passing Interface
An iterative method for mining frequent temporal patterns
EUROCAST'05 Proceedings of the 10th international conference on Computer Aided Systems Theory
Analysis of the effectiveness of G3PARM algorithm
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Towards healthy association rule mining (HARM): a fuzzy quantitative approach
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Playing by the rules: mining query associations to predict search performance
Proceedings of the sixth ACM international conference on Web search and data mining
Finding Associations in Composite Data Sets: The CFARM Algorithm
International Journal of Data Warehousing and Mining
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A well-known approach to Knowledge Discovery in Databases involves the identification of association rules linking database attributes. Extracting all possible association rules from a database, however, is a computationally intractable problem, because of the combinatorial explosion in the number of sets of attributes for which incidence-counts must be computed. Existing methods for dealing with this may involve multiple passes of the database, and tend still to cope badly with densely-packed database records. We describe here a class of methods we have introduced that begin by using a single database pass to perform a partial computation of the totals required, storing these in the form of a set enumeration tree, which is created in time linear to the size of the database. Algorithms for using this structure to complete the count summations are discussed, and a method is described, derived from the well-known Apriori algorithm. Results are presented demonstrating the performance advantage to be gained from the use of this approach. Finally, we discuss possible further applications of the method.