Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Behind-the-scenes data mining: a report on the KDD-98 panel
ACM SIGKDD Explorations Newsletter
OSSM: A Segmentation Approach to Optimize Frequency Counting
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Data Mining: How Research Meets Practical Development?
Knowledge and Information Systems
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Agent-mining interaction: an emerging area
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
Agents and data mining: mutual enhancement by integration
AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
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Many organizations struggle with what to do with the massive amount of data they collect. Although some have touted data mining as the solution, it has failed to have a major impact despite its successes in many areas. One reason is that data mining algorithms weren't designed for the real world-that is, they usually assume a static view of the data and a stable execution environment with abundant resources. The reality, however, is that data constantly change and the execution environment is dynamic. So, it becomes difficult for data mining to truly deliver timely and relevant results. The solution to this might be to combine stream data mining algorithms with intelligent agents, as preliminary results from the Matrix project suggest.