Aiming at the problems of false positive patterns and redundant patterns in tasks of discriminative high utility pattern mining, a discriminative high utility pattern mining algorithm based on unlimited testing and independent growth rate technique — UTDHU (Unlimited Testing for Discriminative High Utility pattern mining) was designed. Firstly, the discriminative high utility patterns that meet utility and difference thresholds were mined from a target transaction set. Then, the redundant patterns were screened out by independent growth rates of patterns which were calculated by constructing a shared tree of prefix-items. Finally, the statistical significance measure p-value for each remaining pattern was calculated by the unlimited testing, and the false positive discriminative high utility patterns were filtered out according to the family wise error rates. Experimental results on four benchmark transaction sets and two synthetic transaction sets show that compared with Hamm, YBHU (Yekutieli-Benjamini resampling for High Utility pattern mining) and other algorithms, the proposed algorithm outputs the least in terms of the number of patterns, with more than 97.8% of tested patterns moved. In terms of mode quality, the proportions of false positive discriminative high utility patterns of the proposed algorithm are less than 5.2% and the classification accuracies of constructed features of the proposed algorithm are at least 1.5 percentage points higher than those of the compared algorithms. Additionally, in terms of running time, although the proposed algorithm is slower than Hamm algorithm, it is faster than the other three algorithms based on statistical significance testing. It can be seen that the proposed algorithm can effectively eliminate a certain number of false positive and redundant discriminative high-utility patterns, exhibits superior mining performance, and achieves higher operational efficiency.