ACM Computing Surveys (CSUR)
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Process mining: a research agenda
Computers in Industry - Special issue: Process/workflow mining
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Structural Patterns for Soundness of Business Process Models
EDOC '06 Proceedings of the 10th IEEE International Enterprise Distributed Object Computing Conference
Process equivalence: comparing two process models based on observed behavior
BPM'06 Proceedings of the 4th international conference on Business Process Management
Conformance testing: measuring the fit and appropriateness of event logs and process models
BPM'05 Proceedings of the Third international conference on Business Process Management
Divide-and-Conquer Strategies for Process Mining
BPM '09 Proceedings of the 7th International Conference on Business Process Management
Projection approaches to process mining using region-based techniques
Data Mining and Knowledge Discovery
Constructing workflow clusters based on appropriateness conformance checker
AIC'10/BEBI'10 Proceedings of the 10th WSEAS international conference on applied informatics and communications, and 3rd WSEAS international conference on Biomedical electronics and biomedical informatics
A Study of Quality and Accuracy Trade-offs in Process Mining
INFORMS Journal on Computing
Data flow-oriented process mining to support security audits
ICSOC'11 Proceedings of the 2011 international conference on Service-Oriented Computing
Application of tree-structured data mining for analysis of process logs in XML format
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Slice, mine and dice: complexity-aware automated discovery of business process models
BPM'13 Proceedings of the 11th international conference on Business Process Management
Hi-index | 0.00 |
Process mining techniques attempt to extract non-trivial and useful information from event logs recorded by information systems. For example, there are many process mining techniques to automatically discover a process model based on some event log. Most of these algorithms perform well on structured processes with little disturbances. However, in reality it is difficult to determine the scope of a process and typically there are all kinds of disturbances. As a result, process mining techniques produce spaghetti-like models that are difficult to read and that attempt to merge unrelated cases. To address these problems, we use an approach where the event log is clustered iteratively such that each of the resulting clusters corresponds to a coherent set of cases that can be adequately represented by a process model. The approach allows for different clustering and process discovery algorithms. In this paper, we provide a particular clustering algorithm that avoids over-generalization and a process discovery algorithm that is much more robust than the algorithms described in literature [1]. The whole approach has been implemented in ProM.