Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
On the discovery of process models from their instances
Decision Support Systems
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Mining most specific workflow models from event-based data
BPM'03 Proceedings of the 2003 international conference on Business process management
A survey on enhanced subspace clustering
Data Mining and Knowledge Discovery
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Process-oriented systems have been increasingly attracting data mining community, due to the opportunities the application of inductive process mining techniques to log data can open to both the analysis of complex processes and the design of new process models. Currently, these techniques focus on structural aspects of the process and disregard data that are kept by many real systems, such as information about activity executors, parameter values, and time-stamps. In this paper, an enhanced process mining approach is presented, where different process variants (use cases) can be discovered by clustering log traces, based on both structural aspects and performance measures. To this aim, an information-theoretic framework is used, where the structural information as well as performance measures are represented by a proper domain, which is correlated to the “central domain” of logged process instances. Then, the clustering of log traces is performed synergically with that of the correlated domains. Eventually, each cluster is equipped with a specific model, so providing the analyst with a compact and handy description of the execution paths characterizing each process variant.