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
The "DGX" distribution for mining massive, skewed data
Proceedings of the seventh 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
Data Mining: An Overview from a Database Perspective
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Mining dynamic databases by weighting
Acta Cybernetica
Post-mining: maintenance of association rules by wieghting
Information Systems
A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
Interactive sequence discovery by incremental mining
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Mining maximal hyperclique pattern: A hybrid search strategy
Information Sciences: an International Journal
Enriching the ER model based on discovered association rules
Information Sciences: an International Journal
Enhancing SWF for incremental association mining by itemset maintenance
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Developing recommender systems with the consideration of product profitability for sellers
Information Sciences: an International Journal
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
Mining high utility patterns in incremental databases
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
FIUT: A new method for mining frequent itemsets
Information Sciences: an International Journal
A decremental algorithm of frequent itemset maintenance for mining updated databases
Expert Systems with Applications: An International Journal
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Measures for comparing association rule sets
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Single-pass incremental and interactive mining for weighted frequent patterns
Expert Systems with Applications: An International Journal
Interactive mining of high utility patterns over data streams
Expert Systems with Applications: An International Journal
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Maintaining frequent itemsets (patterns) is one of the most important issues faced by the data mining community. While many algorithms for pattern discovery have been developed, relatively little work has been reported on mining dynamic databases, a major area of application in this field. In this paper, a new algorithm, namely the Efficient Dynamic Database Updating Algorithm (EDUA), is designed for mining dynamic databases. It works well when data deletion is carried out in any subset of a database that is partitioned according to the arrival time of the data. A pruning technique is proposed for improving the efficiency of the EDUA algorithm. Extensive experiments are conducted to evaluate the proposed approach and it is demonstrated that the EDUA is efficient.