Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 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
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Adaptive Intrusion Detection: A Data Mining Approach
Artificial Intelligence Review - Issues on the application of data mining
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
An Efficient Inductive Learning Method for Object-Oriented Database Using Attribute Entropy
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th 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
An Algorithm for Constrained Association Rule Mining in Semi-structured Data
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Mining Access Patterns Efficiently from Web Logs
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
An Effective Boolean Algorithm for Mining Association Rules in Large Databases
DASFAA '99 Proceedings of the Sixth International Conference on Database Systems for Advanced Applications
mBAR: A Materialized Bitmap Based Association Rule Algorithm
ICDEW '05 Proceedings of the 21st International Conference on Data Engineering Workshops
BitTableFI: An efficient mining frequent itemsets algorithm
Knowledge-Based Systems
Multi-level fuzzy mining with multiple minimum supports
Expert Systems with Applications: An International Journal
Computers & Mathematics with Applications
Discovering Frequent Agreement Subtrees from Phylogenetic Data
IEEE Transactions on Knowledge and Data Engineering
Mining interesting imperfectly sporadic rules
Knowledge and Information Systems
Hardware-Enhanced Association Rule Mining with Hashing and Pipelining
IEEE Transactions on Knowledge and Data Engineering
High Efficiency Association Rules Mining Algorithm for Bank Cost Analysis
ISECS '08 Proceedings of the 2008 International Symposium on Electronic Commerce and Security
Mining protein-protein interaction information on the internet
Expert Systems with Applications: An International Journal
A matrix algorithm for mining association rules
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
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Mining association rule in a large database is a technique for finding relations among attributes. In the last decade, most studies have been devoted to boost the efficiency, but few of them have been concentrated on the analysis of logic correlation among variables. Furthermore, mining association rules in a large database, when applied on a bio-sequence data set, is generally medically irrelevant and difficult to analyze. In this paper, a pre and post-processing approach through discovering a logic correlation rule by combining Apriori-based method and Boolean function simplification technique called Apriori_BFS method is presented. The objective of the proposed method is to effectively reduce the number of rules and present an integration logic correlation rule to readers. The experiment was conducted by using a real-world case, the HIV Drug Resistance Database, and its results unveil that the proposed method, Apriori_BFS, can not only present the logic correlation among variables but also provide more condensed rules than the Apriori method alone.