Asynchronous parallel algorithm for mining association rules on a shared-memory multi-processors
Proceedings of the tenth annual ACM symposium on Parallel algorithms and architectures
Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
DNA-miner: a system prototype for mining DNA sequences
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Parallel data mining for association rules on shared memory systems
Knowledge and Information Systems
Data Mining for Scientific and Engineering Applications
Data Mining for Scientific and Engineering Applications
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Efficiently Mining Maximal Frequent Itemsets
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
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Real World Association Rule Mining
BNCOD 19 Proceedings of the 19th British National Conference on Databases: Advances in Databases
DEMON: Mining and Monitoring Evolving Data
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
Models for association rules based on clustering and correlation
Intelligent Data Analysis
Incrementally maintaining classification using an RDBMS
Proceedings of the VLDB Endowment
Statistical supports for frequent itemsets on data streams
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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Much of the existing work in machine learning and data mining has relied on devising efficient techniques to build accurate models from the data. Research on how the accuracyof a model changes as a function of dynamic updates to the databases is very limited. In this work we show that extracting this information: knowing which aspects of the model are changing; and how theyare changing as a function of data updates; can be verye effective for interactive data mining purposes (where response time is often more important than model qualityas long as model qualityi s not too far off the best (exact) model.In this paper we consider the problem of generating approximate models within the context of association mining, a keyda ta mining task. We propose a new approach to incrementallyg enerate approximate models of associations in evolving databases. Our approach is able to detect how patterns evolve over time (an interesting result in its own right), and uses this information in generating approximate models with high accuracy at a fraction of the cost (of generating the exact model). Extensive experimental evaluation on real databases demonstrates the effectiveness and advantages of the proposed approach.