关键词: 过程挖掘, 日志划分, 信息熵, 轨迹聚类

Abstract: To improve the process discovery,log partitionis is used to divide the raw log data into multiple sub-logs.The existing methods for evaluating log partition are with ground truth,but it is difficult to obtain the marked log data in real life.For this reason,the partition entropy was proposed as a measure of log partition evaluation without ground truth.The trace variants were defined to depict the distribution of each sub-log.The internal entropy and external entropy were proposed to respectively describe the cohesion and divergence among those sub-logs.The penalty factor was used to punish some evaluation methods those blindly catering to the standard of high cohesion and low coupling.The equation of partition entropy was proposed based on internal entropy,external entropy and penalty factor.Experimental results showed the feasibility of the proposed method.

Key words: process mining, log partition, information entropy, trace clustering