A Comparative Study of Dimensionality Reduction Techniques to Enhance Trace Clustering Performances

Technology Management/ Information System/ EntrepreneurshipProcess mining aims at extracting useful information from event logs. Recently, in order to improve processes, several organizations such as high-tech companies, hospitals, and municipalities utilize process mining techniques. Real-life process logs from such organizations are usually very large and complicated, since the process logs in general contain numerous activities which are executed by many employees. Furthermore, lots of real-life process logs generate spaghetti-like process models due to the complexity of processes. Traditional process mining techniques have problems with discovering and analyzing real-life process logs which come from less structured processes. To overcome the weaknesses of traditional process mining techniques, a trace clustering has been developed. The trace clustering splits an event log into several subsets, and each subset contains homogenous cases. Even though the trace clustering is useful to handle complex process logs, it is time-consuming and computationally expensive due to a large number of features generated from complex logs. In this thesis, we applied dimensionality reduction (preprocessing) techniques to the trace clustering in order to reduce the number of features. To validate our approach, we conducted experiments to discover relationships between dimensionality reduction techniques and clustering algorithms, and we performed a case study which involves patient treatment processes of a hospital. Among many dimensionality reduction techniques, we used three techniques namely singular value decomposition (SVD), random projection, and principal components analysis (PCA). The result shows that the trace clustering with dimensionality reduction techniques produce higher average fitness values. Furthermore, processing time of trace clustering is effectively reduced with dimensionality reduction techniques. Moreover, we measured similarity between clustering results to observe the degree of changes in clustering results while applying dimensionality reduction techniques. The similarity is resulted differently according to used clustering algorithm.ope

Generalized alignment-based trace clustering of process behavior

Process mining techniques use event logs containing real process executions in order to mine, align and extend process models. The partition of an event log into trace variants facilitates the understanding and analysis of traces, so it is a common pre-processing in process mining environments. Trace clustering automates this partition; traditionally it has been applied without taking into consideration the availability of a process model. In this paper we extend our previous work on process model based trace clustering, by allowing cluster centroids to have a complex structure, that can range from a partial order, down to a subnet of the initial process model. This way, the new clustering framework presented in this paper is able to cluster together traces that are distant only due to concurrency or loop constructs in process models. We show the complexity analysis of the different instantiations of the trace clustering framework, and have implemented it in a prototype tool that has been tested on different datasets.Peer ReviewedPostprint (author's final draft

Trace Clustering for User Behavior Mining

Business information systems support a large variety of business processes and tasks, yet organizations rarely understand how users interact with these systems. User Behavior Mining aims to address this by applying process mining techniques to UI logs, i.e., detailed records of interactions with a system\u27s user interface. Insights gained from this type of data hold great potential for usability engineering and task automation, but the complexity of UI logs can make them challenging to analyze. In this paper, we explore trace clustering as a means to structure UI logs and reduce this complexity. In particular, we apply different trace clustering approaches to a real-life UI log and show that the cluster-level process models reveal useful information about user behavior. At the same time, we find configurations in which trace clustering fails to generate satisfactory partitions. Our results also demonstrate that recently proposed representation learning techniques for process traces can be effectively employed in a realistic setting

Generalized Alignment-Based Trace Clustering of Process Behavior

International audienceProcess mining techniques use event logs containing real process executions in order to mine, align and extend process models. The partition of an event log into trace variants facilitates the understanding and analysis of traces, so it is a common pre-processing in process mining environments. Trace clustering automates this partition; traditionally it has been applied without taking into consideration the availability of a process model. In this paper we extend our previous work on process model based trace clustering, by allowing cluster centroids to have a complex structure, that can range from a partial order, down to a sub-net of the initial process model. This way, the new clustering framework presented in this paper is able to cluster together traces that are distant only due to concurrency or loop constructs in process models. We show the complexity analysis of the different instantiations of the trace clustering framework, and have implemented it in a prototype tool that has been tested on different datasets

Mining structured Petri nets for the visualization of process behavior

Visualization is essential for understanding the models obtained by process mining. Clear and efficient visual representations make the embedded information more accessible and analyzable. This work presents a novel approach for generating process models with structural properties that induce visually friendly layouts. Rather than generating a single model that captures all behaviors, a set of Petri net models is delivered, each one covering a subset of traces of the log. The models are mined by extracting slices of labelled transition systems with specific properties from the complete state space produced by the process logs. In most cases, few Petri nets are sufficient to cover a significant part of the behavior produced by the log.Peer ReviewedPostprint (author's final draft