Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
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
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
DualMiner: a dual-pruning algorithm for itemsets with constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient Mining of Constrained Correlated Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Efficient data mining for calling path patterns in GSM networks
Information Systems
Efficient closed pattern mining in the presence of tough block constraints
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining spatial association rules in image databases
Information Sciences: an International Journal
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity
Information Sciences: an International Journal
An approach to mining bundled commodities
Knowledge-Based Systems
An efficient algorithm for mining closed inter-transaction itemsets
Data & Knowledge Engineering
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
Rough Set Model for Constraint-based Multi-dimensional Association Rule Mining
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Application of KDD in mechanical structure symmetry design
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
MCFPTree: An FP-tree-based algorithm for multi-constraint patterns discovery
International Journal of Business Intelligence and Data Mining
International Journal of Intelligent Information and Database Systems
Using a projection-based approach to mine frequent inter-transaction patterns
Expert Systems with Applications: An International Journal
Constrained frequent pattern mining on univariate uncertain data
Journal of Systems and Software
Closed inter-sequence pattern mining
Journal of Systems and Software
Key roles of closed sets and minimal generators in concise representations of frequent patterns
Intelligent Data Analysis
Efficient mining of maximal correlated weight frequent patterns
Intelligent Data Analysis
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To improve the effectiveness and efficiency of mining tasks, constraint-based mining enables users to concentrate on mining their interested association rules instead of the complete set of association rules. Previously proposed methods are mainly contributed to handling a single constraint and only consider the constraints which are characterized by a single attribute value. In this paper, we propose an approach to mine association rules with multiple constraints constructed by multi-dimensional attribute values. Our proposed approach basically consists of three phases. First, we collect the frequent items and prune infrequent items according to the Apriori property. Second, we exploit the properties of the given constraints to prune search space or save constraint checking in the conditional databases. Third, for each itemset possible to satisfy the constraint, we generate its conditional database and perform the three phases in the conditional database recursively. Our proposed algorithms can exploit the properties of constraints to prune search space or save constraint checking. Therefore, our proposed algorithm is more efficient than the revised FP-growth and FIC algorithms.