Abstract
In data mining, target data selection is important. The symptom of 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.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
