{"title":"最大模式挖掘方法及其变体分析","authors":"Dávid Gégény, S. Radeleczki","doi":"10.32968/psaie.2022.2.7.","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":117509,"journal":{"name":"Production Systems and Information Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of the Maximal Pattern Mining method and its variants\",\"authors\":\"Dávid Gégény, S. Radeleczki\",\"doi\":\"10.32968/psaie.2022.2.7.\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":117509,\"journal\":{\"name\":\"Production Systems and Information Engineering\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Production Systems and Information Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32968/psaie.2022.2.7.\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Production Systems and Information Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32968/psaie.2022.2.7.","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.