Online analytical mining association rules using Chi-square test
Joseph S. P. Fong, Shi Ming Huang, Hsiang-Yuan Hsueh · International Journal of Business Intelligence and Data Mining · 2007
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.