relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3711/
title: A Compact Trace Representation Using Deep Neural Networks for Process Mining
creator: Bui, Hong Nhung
creator: Vu, Trong-Sinh
creator: Nguyen, Tri Thanh
creator: Nguyen, Thi Cham
creator: Ha, Quang Thuy
subject: Information Technology (IT)
description: — In process mining, trace representation has a significant effect on the process discovery problem, the challenge is to get highly informative but low-dimensional of the vector space from event logs. This is required to improve the quality of the trace clustering problem and generate the process models that are easy to understand. Though traditional trace representation methods have specific good effects, their vector space often has a lot of dimensions. In this paper, we address this problem by proposing a new trace representation method based on the deep neural networks. Experimental results show that our method not only outperforms alternatives but also helps significantly reduce the dimension of feature representation.
date: 2019-10-24
type: Conference or Workshop Item
type: NonPeerReviewed
identifier:   Bui, Hong Nhung and Vu, Trong-Sinh and Nguyen, Tri Thanh and Nguyen, Thi Cham and Ha, Quang Thuy  (2019) A Compact Trace Representation Using Deep Neural Networks for Process Mining.  In: The 11th IEEE International Conference on Knowledge and Systems Engineering, 24-26 October, 2019, Da Nang, Vietnam.