# Efficient False Positive Control Algorithms in Big Data Mining
## 4 Experiments and Performance Analysis
### Distributed PFWER False Positive Control Experiment
#### 4.3.2 Pruning Efficiency Analysis  
(1) Experimental description  
The PFWER false positive control algorithm needs to find all the hypotheses to be tested in the dataset, and these hypotheses to be tested are composed of the patterns mined in the transaction set and their corresponding permuted labels. Therefore, it is necessary to use techniques related to pattern mining. In the computation process, it is found that using [PERSON]'s exact test to calculate the \(p\)-value and using the WY replacement process for false positive control can reduce the computation of PFWER false positive control by some pruning operations and speeding up the computation, which does not affect the computation results.  
The purpose of the experimental tests in this section is to verify the effect of pruning operations on the algorithm. From the above experimental description, it can be seen that the execution of the pruning operation reduces the number of patterns to be calculated for PFWER false positive control and does not affect the false positive control effect. Therefore, the experiments in this section will verify the efficiency of the pruning operation in terms of both the number of patterns that need to be computed before and after the pruning operation and the change in the significance threshold.  
(2) Experimental analysis  
The purple bars in Figure 11 show the number of patterns mined before the pruning operation, and the green bars show the number of patterns mined after the pruning operation. The experimental results show that the use of the pruning operation in the calculation of the PFWER false positive control can effectively reduce the number of patterns calculated, thus reducing the number of \(p\)-values that need to be calculated by [PERSON]'s exact test and thus can effectively improve the efficiency of the PFWER false positive control.  
Table 9 shows the effect of pruning on the run speed of different datasets before and after the pruning operation, and it can be seen from the data in the table that for most of the datasets, the pruning operation can improve the run efficiency.  
Figure 11: The number of modes before and after pruning operations in different datasets.  
Figure 10: Run time changes.  
Figure 12 represents the changes in the support counts of different datasets before and after the pruning operation. From the experimental results in Figure 12, we can see that the results calculated by the PFWER false positive control algorithm before and after performing the pruning operation are basically the same, thus verifying the correctness of the pruning operation.  
Figure 13 shows the comparison of the significance thresholds of the PFWER false positive control after performing pruning operations with and without the pruning operation on different datasets, with the vertical coordinate as the logarithm with base 10. Since the PFWER false positive control performs random permutations of jr times labels that affect the final significance threshold results, it is acceptable to have some deviation in the significance thresholds after performing the pruning operation with and without pruning on individual datasets.