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Hence, a strong coupling relationship between infrequent behavior and data flow exists. Furthermore, some infrequent behaviors may reveal very important information about the process. Thus, not all infrequent behaviors should be disregarded as noise, and identifying infrequent but correct behaviors in the event log is vital to process mining from the perspective of data flow. Existing process mining approaches construct a process model from frequent behaviors in the event log, mostly concentrating on control flow only, without considering infrequent behavior and data flow information. In this paper, we focus on data flow to extract infrequent but correct behaviors from logs. For an infrequent trace, frequent patterns and interactive behavior profiles are combined to find out which part of the behavior in the trace occurs in low frequency. And, conditional dependency probability is used to analyze the influence strength of the data flow information on infrequent behavior. An approach for identifying effective infrequent behaviors based on the frequent pattern under data awareness is proposed correspondingly. Subsequently, an optimization approach for mining of process models with infrequent behaviors integrating data flow and control flow is also presented. The experiments on synthetic and real-life event logs show that the proposed approach can distinguish effective infrequent behaviors from noise compared with others. The proposed approaches greatly improve the fitness of the mined process model without significantly decreasing its precision.<\/jats:p>","DOI":"10.1155\/2021\/8874316","type":"journal-article","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T23:38:51Z","timestamp":1620085131000},"page":"1-17","source":"Crossref","is-referenced-by-count":1,"title":["An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow"],"prefix":"10.1155","volume":"2021","author":[{"given":"Li-li","family":"Wang","sequence":"first","affiliation":[{"name":"College of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232000, China"},{"name":"The Key Laboratory of Embedded System and Service Computing Ministry of Education (Tongji University), Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8531-7215","authenticated-orcid":true,"given":"Xian-wen","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esther","family":"Asare","sequence":"additional","affiliation":[{"name":"College of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Huan","sequence":"additional","affiliation":[{"name":"College of Mathematics and Big Data, Anhui University of Science and Technology, Huainan 232000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-662-49851-4","volume-title":"Process Mining-Data Science in Action","author":"W. 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