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Improving Process Mining with Trace Clustering
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 Title & Authors
Improving Process Mining with Trace Clustering
Song, Min-Seok; Gunther, C.W.; van der Aalst, W.M.P.; Jung, Jae-Yoon;
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 Abstract
Process mining aims at mining valuable information from process execution results (called "event logs"). Even though process mining techniques have proven to be a valuable tool, the mining results from real process logs are usually too complex to interpret. The main cause that leads to complex models is the diversity of process logs. To address this issue, this paper proposes a trace clustering approach that splits a process log into homogeneous subsets and applies existing process mining techniques to each subset. Based on log profiles from a process log, the approach uses existing clustering techniques to derive clusters. Our approach are implemented in ProM framework. To illustrate this, a real-life case study is also presented.
 Keywords
Process Mining;Trace Clustering;Workflow;Data Mining;SOM;
 Language
Korean
 Cited by
1.
프로세스 마이닝 기법을 활용한 고장 수리 프로세스 분석,최상현;한관희;임건훈;

한국콘텐츠학회논문지, 2013. vol.13. 4, pp.399-406 crossref(new window)
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