Genetic process hybrid mining algorithm based on trace clustering population

被引：0
|
作者：
Tang Y. [1 ]
Zhu R. [2 ,3 ]
Li T. [2 ,4 ]
Nan F. [3 ]
Zheng M. [1 ]
Ma Z. [3 ]
机构：
[1] School of Information, Yunnan University, Kunming
[2] Key Laboratory in Software Engineering of Yunnan Province, Kunming
[3] School of Software, Yunnan University, Kunming
[4] School of Big Data, Yunnan Agricultural University, Kunming
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2020年 / 26卷 / 06期
关键词：
Genetic process mining algorithm; Inductive miner algorithm; Process mining; Trace cluster;
D O I：
10.13196/j.cims.2020.06.008
中图分类号：
学科分类号：
摘要：
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. © 2020, Editorial Department of CIMS. All right reserved.
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页码：1510 / 1524
页数：14
相关论文
共 36 条
  • [11] BERGENTHUM R, DESEL J, LORENZ R, Et al., Process mining based on regions of languages, Proceedings of the 5th International Conference on Business Process Management, pp. 375-383, (2007)
  • [12] BUIJS J C A M, VAN DONGEN B F, VAN DER AALST W M P., A genetic algorithm for discovering process trees, Proceedings of the 2012 IEEE Congress on Evolutionary Computation, pp. 1-8, (2012)
  • [13] SONG M, GUNTHER C W, VAN DER AALST W M P., Trace clustering in process mining, Proceedings of the International Conference on Business Process Management, pp. 109-120, (2009)
  • [14] JAGADEESH CHANDRA BOSER. P, VAN DER AALST W M P., Trace clustering based on conserved patterns:towards achieving better process models, Proceedings of the International Conference on Business Process Management, pp. 170-181, (2010)
  • [15] LEONI M D, MAGGI F M, VAN DER AALST W M P., Aligning event logs and declarative process models for conformance checking, Proceedings of the International Conference on Business Process Management, pp. 82-97, (2012)
  • [16] CORTTADELL J, KISHINEVSKY M, LAVAGNO L, Et al., Deriving Petri nets from finite transition systems, IEEE Transactions on Computers, 47, 8, pp. 859-882, (1998)
  • [17] FAHLAND D, VAN DER AALST W M P., Model repair - aligning process models to reality, Information Systems, 47, 1, pp. 220-243, (2015)
  • [18] HOMPES B B, BUIJS J J, VAN DER AALST W M P, Et al., Discovering deviating cases and process variants using trace clustering
  • [19] HOMPES B F A, BUIJS J C A M, VAN DER AALST W M P, Et al., Detecting change in processes using comparative trace clustering
  • [20] VAN DER AALST W M P, BUIJS J, DONGEN B V., Towards improving the representational Bias of process mining, pp. 39-54, (2011)
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