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
Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining and Machine Oriented Modeling: A Granular Computing Approach
Applied Intelligence
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Frequent Itemsets Using Support Constraints
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from 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 incremental data mining algorithm using pre-large itemsets
Intelligent Data Analysis
Expert Systems with Applications: An International Journal
Multi-level fuzzy mining with multiple minimum supports
Expert Systems with Applications: An International Journal
MRM: A matrix representation and mapping approach for knowledge acquisition
Knowledge-Based Systems
An approach to mining bundled commodities
Knowledge-Based Systems
SemGrAM: integrating semantic graphs into association rule mining
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Towards personalized recommendation by two-step modified Apriori data mining algorithm
Expert Systems with Applications: An International Journal
Hiding Frequent Patterns under Multiple Sensitive Thresholds
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Development of an intelligent quality management system using fuzzy association rules
Expert Systems with Applications: An International Journal
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Mining periodic-frequent patterns with maximum items' support constraints
Proceedings of the Third Annual ACM Bangalore Conference
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Mining generalized association rules with quantitative data under multiple support constraints
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Mining multiple-level association rules under the maximum constraint of multiple minimum supports
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
An efficient approach to mine rare association rules using maximum items' support constraints
BNCOD'10 Proceedings of the 27th British national conference on Data Security and Security Data
Information Sciences: an International Journal
An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports
Journal of Systems and Software
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Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most of the previous approaches set a single minimum support threshold for all the items or itemsets. But in real applications, different items may have different criteria to judge its importance. The support requirements should then vary with different items. In this paper, we provide another point of view about defining the minimum supports of itemsets when items have different minimum supports. The maximum constraint is used, which is well explained and may be suitable to some mining domains. We then propose a simple algorithm based on the Apriori approach to find the large-itemsets and association rules under this constraint. The proposed algorithm is easy and efficient when compared to Wang et al.'s under the maximum constraint. The numbers of association rules and large itemsets obtained by the proposed mining algorithm using the maximum constraint are also less than those using the minimum constraint. Whether to adopt the proposed approach thus depends on the requirements of mining problems. Besides, the granular computing technique of bit strings is used to speed up the proposed data mining algorithm.