Algorithms in C++
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Communications of the ACM
Beyond market baskets: generalizing association rules to correlations
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
Efficiently mining long patterns from databases
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
A data mining application: customer retention at the Port of Singapore Authority (PSA)
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
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable association-based text classification
Proceedings of the ninth international conference on Information and knowledge management
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Discrete Mathematics
Digital Systems: Principles and Applications
Digital Systems: Principles and Applications
A pattern decomposition algorithm for data mining of frequent patterns
Knowledge and Information Systems
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on 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
Mining Frequent Closed Itemsets with the Frequent Pattern List
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Ascending Frequency Ordered Prefix-tree: Efficient Mining of Frequent Patterns
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Association Analysis with One Scan of Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
HUC-Prune: an efficient candidate pruning technique to mine high utility patterns
Applied Intelligence
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Mining frequent patterns is a fundamental and crucial task in data-mining problems. The algorithms reported in the literature for mining frequent patterns can be classified into two approaches: the candidate generation-and-test approach (for example, the Apriori algorithm) and the pattern-growth approach (such as the FP-growth algorithm). The approaches both suffered from the problems that their speed is slow for large databases. This paper proposes a novel and simple approach, which does not belong to the above two approaches. This approach treats the database as a stream of data and finds frequent patterns by scanning the database only once. In addition, the approach can incrementally mine frequent patterns if the database is updated or inserted subsequently. Three versions of the approach (i.e., mapping-table, transformation-function, and logic-circuit) are provided. The logic-circuit version is the first one that mines frequent patterns by simple logic gates, and the modeling of this version shows its speed is thousands of times faster than that of the FP-growth algorithm. Analyses and simulations of the approach are also performed. Analyses show that the transformation-function version is much better than the Apriori and FP-growth ones in storage complexity. Simulation results show that the mapping-table version is comparable to the FP-growth algorithm in execution time.