Incremental maintenance of views with duplicates
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Information Systems Reengineering
Information Systems Reengineering
Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications
Data Mining and Knowledge Discovery
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Anthill: A Scalable Run-Time Environment for Data Mining Applications
SBAC-PAD '05 Proceedings of the 17th International Symposium on Computer Architecture on High Performance Computing
Parallel Leap: Large-Scale Maximal Pattern Mining in a Distributed Environment
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 1
Exploring the Capabilities of Mobile Agents in Distributed Data Mining
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
Distributed Mining of Constrained Patterns from Wireless Sensor Data
WI-IATW '06 Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology
A New Algorithm for Mining Fuzzy Association Rules in the Large Databases Based on Ontology
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
An incremental data mining algorithm for discovering web access patterns
International Journal of Business Intelligence and Data Mining
Association-rule knowledge discovery by using a fuzzy mining approach
International Journal of Business Intelligence and Data Mining
Exploiting data preparation to enhance mining and knowledgediscovery
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Techniques for finding similarity knowledge in OLAP reports
Expert Systems with Applications: An International Journal
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In data mining, target data selection is important. The symptom of "garbage in and garbage out" is avoided to derive effective business rules in knowledge discovery in database. Chi-Square test is useful to eliminate irrelevant data before data mining processing due to wrong degrees of freedom, untested hypothesis, inconsistent estimation, inefficient method, data redundancy, data overdue, and data heterogeneity. This paper offers an online analytical processing method to derive association rules for the filtered Chi-Square tested data. The process applies a Frame metadata to trigger the Chi-Square testing for the update of the source data, and to derive rules continuously.