Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
In search of reliable usage data on the WWW
Selected papers from the sixth international conference on World Wide Web
WebAssist: a user profile specific information retrieval assistant
WWW7 Proceedings of the seventh international conference on World Wide Web 7
The Web supercomputing environment
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Towards a better understanding of Web resources and server responses for improved caching
WWW '99 Proceedings of the eighth international conference on World Wide Web
Unintrusive customization techniques for Web advertising
WWW '99 Proceedings of the eighth international conference on World Wide Web
Improving Web information systems with navigational patterns
WWW '99 Proceedings of the eighth international conference on World Wide Web
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Item-Set Tree: A Data Structure for Data Mining
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Data mining for path traversal patterns in a web environment
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
Mining the acceleration-like association rules
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
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Business information received from advanced data analysis and data mining is a critical success factor for companies wishing to maximize competitive advantage. The use of traditional tools and techniques to discover knowledge is ruthless and does not give the right information at the right time. Data mining should provide tactical insights to support the strategic directions. In this paper, we introduce a dynamic approach that uses knowledge discovered in previous episodes. The proposed approach is shown to be effective for solving problems related to the efficiency of handling database updates, accuracy of data mining results, gaining more knowledge and interpretation of the results, and performance. Our results do not depend on the approach used to generate itemsets. In our analysis, we have used an Apriori-like approach as a local procedure to generate large itemsets. We prove that the Dynamic Data Mining algorithm is correct and complete.