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 frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
The study of an ordered minimal perfect hashing scheme
Communications of the ACM
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Mining Association Rules: A Continuous Incremental Updating Technique
WISM '10 Proceedings of the 2010 International Conference on Web Information Systems and Mining - Volume 01
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In the literatures, hash-based association rule mining algorithms are more efficient than Apriori-based algorithms, since they employ hash functions to generate candidate itemsets efficiently. However, when the dataset is updated, the whole hash table needs to be reconstructed. In this paper, we propose an incremental mining algorithm based on minimal perfect hashing. In our algorithm, each candidate itemset is hashed into a hash table, and their minimum support value can be verified directly by a hash function for latter mining process. Even though new items are added, the structure of the proposed hash does not need to be reconstructed. Therefore, experimental results show that the proposed algorithm is more efficient than other hash-based association rule mining algorithms, and is also more efficient than other Apriori-based incremental mining algorithms for association rules, when the database is dynamically updated.