Multi-View Trace Clustering Based on Graph Convolutional Networks in Process Mining

• Published in: IEEE transactions on services computing Vol. 18; no. 4; pp. 2226 - 2237
• Main Authors: Lin, Leilei, Cao, Yunuo, Chen, Wenlong, Zong, Zan, Qian, Chen, Wen, Lijie
• Format: Journal Article
• Language: English
• Published: IEEE 01.07.2025
• Subjects: Accuracy; Clustering algorithms; Clustering methods; Complexity theory; Fingerprint recognition; Graph convolutional networks; Information systems; Lifting equipment; Process mining; representation learning; service quality; trace clustering; Vectors
• ISSN: 1939-1374; 2372-0204
• DOI: 10.1109/TSC.2025.3577480

Process mining techniques can extract process models from event logs produced by information systems. However, in flexible environments, simply using existing methods often leads to complex process models that are hard to understand, due to the less structured and greater complexity of processes in real life. Trace clustering is a pre-processing technique that enhances the effectiveness of model mining by partitioning similar behaviors in logs. In this article, we present a Multi-view Trace Clustering method, named MTC, that improves the homogeneity of trace subclusters. Our method consists of three parts: (1) We use trace profiles to depict the traces from different views, and each profile would be transformed into a graph based on the k-nearest neighbor algorithm; (2) A fusion graph is designed to capture the information among these graphs based on an attention coefficient matrix, and then the graph convolutional networks are used to encode all graphs for obtaining the common representation; (3) We also enhance the characterization of the common representation with an inner decoder. Finally, we adopt k-means to cluster the traces in the log based on the common representation. Extensive experiments using multiple datasets illustrate that MTC significantly surpasses state-of-the-art trace clustering methods.