最大模式挖掘方法及其变体分析

Dávid Gégény, S. Radeleczki
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引用次数: 0

摘要

在本文中,我们在过程挖掘的框架内研究了由Liesaputra等人在[1]中引入的最大模式挖掘方法。该方法对结构相似的迹线构造一个过渡图,即标记有向图。该算法背后的思想是分析事件日志中的轨迹,识别循环、并行事件和它们之间的可选性,以确定最大模式。在[1]中,作者为其算法的骨架提供了一个伪代码,并讨论了一些部分,但其他部分没有详细说明。在这里,我们简要讨论了算法的步骤,并对[1]中没有解释的步骤进行了详细阐述。我们引入了一些新的子程序来处理循环、并行和可选序列。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of the Maximal Pattern Mining method and its variants
In this paper, within the framework of process mining we examine the Maximal Pattern Mining method introduced by Liesaputra et al. in [1]. This method constructs a transition graph, i.e. a labelled directed graph for traces with similar structure. The idea behind the algorithm is to analyze the traces in the event log, identify loops, parallel events and optionality between them, in order to determine the maximal patterns. In [1], the authors provide a pseudo code for the skeleton of their algorithm and discuss some parts, but other parts are not detailed. Here, we briefly discuss the steps of the algorithm and elaborate the steps that are not explained in [1]. We introduce some new subroutines to handle the loops, parallel and optional sequences.
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