挖掘决策是为了发现非自由选择结构中决策点之间的规则关系

R. Sarno, Putu Linda Indita Sari, Dwi Sunaryono, B. Amaliah, I. Mukhlash
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引用次数: 6

摘要

决策挖掘是流程挖掘和机器学习算法的结合,用于检索有关业务流程中的数据属性如何影响案例路由的信息。它通过在petri-net工作流模型中寻找异或分割来分析决策点,并基于可用属性使用决策树检查每个选择的规则。每个决策点的规则基于属性对案例的影响。同时,非自由选择结构是选择和同步的混合,这将在工作流中产生有限的选择。然后,选择的限制将影响使用决策挖掘技术在非自由选择结构中发现的规则。规则的局限性使得检查工作流中规则之间的关系成为可能。这些规则之间的关系将显现出非自由选择构式的某种性质。非自由选择结构中两个决策点的规则将具有相似性。对此,当在决策挖掘过程中发现相同的规则时,我们可以确定决策点具有非自由选择关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining decision to discover the relation of rules among decision points in a non-free choice construct
Decision mining is a combination of process mining and machine learning algorithms to retrieve information on how data attributes in a business process affect routing of a case. It analyzes decision point by looking for XOR-splits in petri-net workflow model and examining rules for each choice based on available attributes using decision tree. The rules for each decision point are based on the attribute's influence to the case. Meanwhile, a non-free choice construct is a mixture of choice and synchronization, which will create limited choices in the workflow. The limitation of choice will then affect the rules found in non-free choice construct using decision mining technique. Limitation of rules makes it possible to examine the relation among rules in the workflow. The relation of these rules will emerge a certain property of a non-free choice construct. Rules for two decision points within a non-free choice construct will have similarities. Regarding to this, when the same rule is found during a decision mining process, we can determine that the decision points have a non-free choice relationship.
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