基于分类器和聚类器网络的预测过程挖掘：PEDF模型
Predictive process mining by network of classifiers and clusterers: the PEDF model
摘要：
在这项研究中，提出了一个模型来学习事件日志和预测系统的未来事件。所提出的PEDF模型基于事件的序列、持续时间和额外特征进行学习。PEDF模型由标准聚类器和分类器组成的网络构建，具有很高的迭代更新灵活性。该模型需要从日志文件中提取两组数据，即，过渡差异和累积特征。该模型具有一层记忆，这意味着每个转换都依赖于当前事件和前一个事件。为了评估所提出的模型的性能，它是比较的递归神经网络和序贯预测模型，它优于他们。由于事件日志预测模型缺少性能度量，提出了三种度量方法。
英文摘要：
In this research, a model is proposed to learn from event log and predict future events of a system. The proposed PEDF model learns based on events' sequences, durations, and extra features. The PEDF model is built by a network made of standard clusterers and classifiers, and it has high flexibility to update the model iteratively. The model requires to extract two sets of data from log files i.e., transition differences, and cumulative features. The model has one layer of memory which means that each transition is dependent on both the current event and the previous event. To evaluate the performance of the proposed model, it is compared to the Recurrent Neural Network and Sequential Prediction models, and it outperforms them. Since there is missing performance measure for event log prediction models, three measures are proposed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
