Process Mining Based on Clustering: A Quest for Precision

Medeiros, Ana Karla Alves de; Guzzo, Antonella; Greco, Gianluigi; Aalst, Wil M. P. van der; Weijters, A. J. M. M.; Dongen, Boudewijn F. van; Saccà, Domenico

doi:10.1007/978-3-540-78238-4_4

Cited by 97 publications

(80 citation statements)

References 5 publications

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“…Several clustering techniques can be used for this purpose, such as the Disjunctive Workflow Schema (DWS) plug-in [35] which can be seen as an extension of the Heuristic Miner plug-in. It uses the Heuristic Miner to construct the initial process model; however, this model is iteratively refined and clustered with a k-means algorithm.…”

Section: Clustering Techniquesmentioning

confidence: 99%

Business process analysis in healthcare environments: A methodology based on process mining

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Ferreira

2012

Information Systems

387

272

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Performing business process analysis in healthcare organizations is particularly difficult due to the highlydynamic, complex, ad-hoc, and multi-disciplinary nature of healthcare processes. Process mining is a promising approach to obtain a better understanding about those processes by analyzing event data recorded in healthcare information systems. However, not all process mining techniques perform well in capturing the complex and ad-hoc nature of clinical workflows. In this work we introduce a methodology for the application of process mining techniques that leads to the identification of regular behavior, process variants, and exceptional medical cases. The approach is demonstrated in a case study conducted at a hospital emergency service. For this purpose, we implemented the methodology in a tool that integrates the main stages of process analysis. The tool is specific to the case study, but the same methodology can be used in other healthcare environments.

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Section: Clustering Techniquesmentioning

confidence: 99%

Business process analysis in healthcare environments: A methodology based on process mining

Rebuge

Ferreira

2012

Information Systems

387

272

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“…For example, working slot 3 starts during working slot 2 while the working slots 6 and 7 are completely subsumed in working slot 5 . As another example, working slot 9 starts in working slot 8 and ends in working slot 10 . It is important to note that these working slots may correspond to activity executions for different cases, but all are executed by the same resource.…”

Section: Resource-activity Execution Time Analysismentioning

confidence: 99%

“…There are two primary reasons for such a spaghetti-like model: (i) the event log is heterogenous and (ii) there is a lot of concurrency in the process. Process mining results can be improved by partitioning the event log into homogeneous subsets of cases [6][7][8][9][10]. We use the classification defined in Section 1 and consider clusters of homogenous cases for control-flow analysis.…”

Section: Control-flow Analysismentioning

confidence: 99%

Process Mining Applied to the BPI Challenge 2012: Divide and Conquer While Discerning Resources

Bose

Aalst

2013

Business Process Management Workshops

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Abstract. A real-life event log, taken from a Dutch financial institute, is analyzed using state-of-the-art process mining techniques. The log contains events related to loan/overdraft applications of customers. We propose a hierarchical decomposition of the log into homogenous subsets of cases based on characteristics such as the final decision, offer, and suspicion of fraud. These subsets are used to uncover interesting insights. The event log in its entirety and the homogeneous subsets are analyzed using various process mining techniques. In this paper, we present results related to (a) resource perspective and their influence on execution/turnaround times of activities, (b) control-flow perspective, and (c) process diagnostics. A dedicated ProM 1 plug-in developed for this challenge allows for a comprehensive analysis of the resource perspective. For the analysis of control-flow and process diagnostics, we use recent, but pre-existing, ProM plug-ins. As the evaluation shows, our mix of techniques is able to uncover many interesting findings and could be used to improve the underlying loan/overdraft application handling process.

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“…As illustrated in this paper, traditional process mining is different from process variant mining due to its different goal and inputs. Some improvements of process mining algorithms have been made to enhance their performance ( e.g., DWS Mining [17]), but they are still different from variant mining due to their different goals and inputs. A few techniques have been proposed to learn from process variants by mining change primitives.…”

Section: Related Workmentioning

confidence: 99%

Mining Based on Learning from Process Change Logs

Reichert

Wombacher

2009

Business Process Management Workshops

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Abstract. In today's dynamic business world economic success of an enterprise increasingly depends on its ability to react to internal and external changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process changes. This, in turn, leads to a large number of process variants, which are created from the same original model, but might slightly differ from each other. This paper deals with issues related to the mining of such process variant collections. Our overall goal is to learn from process changes and to merge the resulting model variants into a generic process model in the best possible way. By adopting this generic process model in the PAIS, future costs of process change and need for process adaptations will decrease. We compare process variant mining with conventional process mining techniques, and show that it is additionally needed to learn from process changes.

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Process Mining Based on Clustering: A Quest for Precision

Cited by 97 publications

References 5 publications

Business process analysis in healthcare environments: A methodology based on process mining

Business process analysis in healthcare environments: A methodology based on process mining

Process Mining Applied to the BPI Challenge 2012: Divide and Conquer While Discerning Resources

Mining Based on Learning from Process Change Logs

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