A Compact Trace Representation Using Deep Neural Networks for Process Mining

Abstract

In process mining, trace representation has a significant effect on the process discovery problem. The challenge is to get highly informative but low-dimensional vector space from event logs. This is required to improve the quality of the trace clustering problem for generating the process models clear enough to inspect. Though traditional trace representation methods have specific advantages, their vector space often has a big number of dimensions. In this paper, we address this problem by proposing a new trace representation method based on the deep neural networks. Experimental results prove our proposal not only is better than the alternatives, but also significantly helps to reduce the dimension of trace representation.