C4.5: programs for machine learning
C4.5: programs for machine learning
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 association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Sliding-window filtering: an efficient algorithm for incremental mining
Proceedings of the tenth international conference on Information and knowledge management
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
An Efficient Algorithm for Incremental Mining of Association Rules
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A Transaction Mapping Algorithm for Frequent Itemsets Mining
IEEE Transactions on Knowledge and Data Engineering
EDUA: An efficient algorithm for dynamic database mining
Information Sciences: an International Journal
An incremental mining algorithm for association rules based on minimal perfect hashing and pruning
APWeb'12 Proceedings of the 14th international conference on Web Technologies and Applications
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Frequent pattern mining plays an important role in the data mining community since it is usually a fundamental step in various mining tasks. However, maintenance of frequent patterns is very expensive in the incremental database. In addition, the status of a pattern changes with time. In other words, a frequent pattern is possible to become infrequent, and vice versa. In order to exactly find all frequent patterns, most algorithms have to scan the original database completely whenever an update occurs. In this paper, we propose a new algorithm iTM, stands for incremental Transaction Mapping algorithm for incremental frequent pattern mining without rescanning the whole database. It transfers the transaction dataset to the vertical representation such that the incremental dataset can be integrated to the original database easily. As demonstrated in our experiments, the proposed method is very efficient and suitable for mining frequent patterns in the incremental database.