Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Pincer-Search: An Efficient Algorithm for Discovering the Maximum Frequent Set
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
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
AntWeb—The Adaptive Web Server Based on the Ants' Behavior
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Application of particle swarm optimization to association rule mining
Applied Soft Computing
A new logic correlation rule for HIV-1 protease mutation
Expert Systems with Applications: An International Journal
Multi-objective PSO algorithm for mining numerical association rules without a priori discretization
Expert Systems with Applications: An International Journal
Mining association rules with single and multi-objective grammar guided ant programming
Integrated Computer-Aided Engineering
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In the field of data mining, an important issue for association rules generation is frequent itemset discovery, which is the key factor in implementing association rule mining. Therefore, this study considers the user's assigned constraints in the mining process. Constraint-based mining enables users to concentrate on mining itemsets that are interesting to themselves, which improves the efficiency of mining tasks. In addition, in the real world, users may prefer recording more than one attribute and setting multi-dimensional constraints. Thus, this study intends to solve the multi-dimensional constraints problem for association rules generation. The ant colony system (ACS) is one of the newest meta-heuristics for combinatorial optimization problems, and this study uses the ant colony system to mine a large database to find the association rules effectively. If this system can consider multi-dimensional constraints, the association rules will be generated more effectively. Therefore, this study proposes a novel approach of applying the ant colony system for extracting the association rules from the database. In addition, the multi-dimensional constraints are taken into account. The results using a real case, the National Health Insurance Research Database, show that the proposed method is able to provide more condensed rules than the Apriori method. The computational time is also reduced.