An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Sliding-window filtering: an efficient algorithm for incremental mining
Proceedings of the tenth international conference on Information and knowledge management
Parallel Mining of 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
On Mining General Temporal Association Rules in a Publication Database
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Incremental mining of sequential patterns in large databases
Data & Knowledge Engineering
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
A fast algorithm for mining share-frequent itemsets
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
A two-phase algorithm for fast discovery of high utility itemsets
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining top-K high utility itemsets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
On-shelf utility mining with negative item values
Expert Systems with Applications: An International Journal
Incorporating frequency, recency and profit in sequential pattern based recommender systems
Intelligent Data Analysis
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Temporal data mining is the activity of finding interesting correlations or patterns in large temporal data sets. On the other hand, utility mining aims at identifying the itemsets with high utilities. In 2006, Tseng et al. introduced the temporal utility mining which is extended from both temporal association rule mining and utility mining. In this study, we investigated the incremental utility mining which can identify all high temporal utility itemsets in a specified time period on an incremental transaction database. Two efficient algorithms, Incremental Utility Mining (IUM) and Fast Incremental Utility Mining (FIUM), were proposed. The experimental results also showed that both algorithms are efficient.