Sentence examples for minimum confidence constraint from inspiring English sources

Based on frequent itemsets, association rules are mined, where the minimum confidence constraint is other primitive one.

It utilizes a series of high quality class association rules (CARs) mined from the training dataset upon predefined minimum support and confidence constraints to build highly accurate classifiers [11].

With a pair of minimum confidence (MC) and minimum support (MS), the size of (mathrm{L}_{0}) and ({R}_{0}) constraints ranges from 4 to 22 % of (vert {mathcal {A}}^{{mathcal {F}}}vert ) (step 2%%).

Here, minimum confidence value is 0.7.

These algorithms are based primarily on minimum support and the minimum confidence.

Two measures are required: the minimum support and the minimum confidence, Table 6.

To extract the association rules, two measures are required: the minimum support and the minimum confidence.

If the confidence is not less than the minimum confidence threshold, we have a significant sequential rule.

For instance, if the confidence of {1,3,6} ≥ {g} is bigger than the minimum confidence, then we construct this association rule.

To explain the efficiency of MAR-MinSC in comparison with those of PP-MAR-MinSC-2 and PP-MAR-MinSC-1, we also take into account the percent ratio of the number of redundant candidate rules (not satisfying the constraints) to the total of all generated rules after executing PP-MAR-MinSC-2 and PP-MAR-MinSC-1 on a given triple of dataset, minimum support and minimum confidence, called DS-MS-MC.

Furthermore, the GMAR algorithm prunes a large amount of irrelevant rules based on the minimum confidence.