Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Borders: An Efficient Algorithm for Association Generation in Dynamic Databases
Journal of Intelligent Information Systems
Efficient Mining of Association Rules in Distributed Databases
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
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
A Low-Scan Incremental Association Rule Maintenance Method Based on the Apriori Property
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Mining Incremental Association Rules with Generalized FP-Tree
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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Association rules identify associations among data items and were introduced in [1]. A detailed discussion on association rules can be found in [2], [8]. One important step in Association rule mining is to find frequent itemsets. Most of the algorithms to find frequent itemsets deal with the static databases. There are very few algorithms that deal with dynamic(incremental) databases. The most classical algorithm to find frequent itemsets in dynamic database is Borders algorithm [7]. But the Borders algorithm is suitable for centralized databases. This paper presents a modified version of the Borders algorithm, called Distributed Borders, which is suitable for Distributed Dynamic databases.