关键词: 过程挖掘, 归纳挖掘算法, 轨迹聚类, 遗传过程挖掘算法

Abstract: Genetic process mining algorithm uses model quality guide the model mining,continuously optimize the model while mining the model.Therefore,it is easier to generate a high-quality process model by comparing to other mining algorithms.However,its mining efficiency is extremely low for large event logs due to the characteristics of iterative discovery.To solve above problems,Genetic process hybrid Mining algorithm based on Trace Clustering population (GMTC) was proposed.GMTC divided the event log by trace clustering,which could simplify the mining environment.Inductive Miner (IM) algorithm was used to prepare high-quality initial population for genetic mining algorithm.Genetic operators had been optimized using the model deviation information so that the mutation operation changed from random to directed,which could improve the comprehensive quality of the population effectively.Based on the PLG generated simulation log,the real log of a municipal government building permit application process and six public data sets,the experimental results showed that the proposed algorithm had a better improvement in both mining efficiency and model quality.

Key words: process mining, inductive miner algorithm, trace cluster, genetic process mining algorithm