流程模型挖掘算法能够从事件日志中挖掘流程模型，不同流程模型挖掘算法处理事件日志的能力不同。目前，大量涉及流程模型挖掘算法评价的工作大都是间接评价，而间接评价存在局限性。针对这一问题，将可靠性作为模型挖掘算法的重要直接评价指标，提出一种模型挖掘算法可靠性评价的方法，用于直接评价模型挖掘算法的性能。该方法对原始事件日志进行增量预处理以得到增量子日志集合；使用模型挖掘算法对增量子日志和原始事件日志进行处理，得到流程模型；最后，通过质量评估对业务流程模型挖掘算法的可靠性进行评价。基于公开的9个仿真事件日志和4个真实事件日志，从弱可靠性、噪声干扰可靠性和强可靠性3个方面对多个模型挖掘算法进行实验，实验结果表明：Heuristic Miner、Inductive Miner-infrequent、Inductive Miner和Alpha Miner可靠性值依次为4、3.2、2.4和1.6，可靠性值越高，算法的可靠性越强。可见本文方法能够有效地评价算法的可靠性。

Process model discovery algorithms are capable of extracting process models from event logs, but different algorithms have varying capabilities in handling event logs. Currently, most research on evaluating these algorithms involves indirect evaluation methods, which have limitations. To address this issue, a method was proposed to directly evaluate the reliability of process model discovery algorithms, using reliability as an important evaluation metric. The original event log was preprocessed to obtain an incremental sub-log collection, the process model discovery algorithm was applied to the incremental sub-logs and the original event log to obtain process models, and the reliability of the business process model discovery algorithm was evaluated through quality assessment. Based on nine public simulation event logs and four real event logs, multiple model discovery algorithms were experimented on from the aspects of weak reliability, noise interference reliability, and strong reliability. The experimental results showed that the reliability values of Heuristic Miner, Inductive Miner-infrequent, Inductive Miner, and Alpha Miner were 4, 3.2, 2.4, and 1.6, respectively. Higher reliability values indicated stronger reliability of the algorithms. Thus, the proposed method can effectively evaluate the reliability of the algorithms.