TY  - CONF
ID  - SisLab3711
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3711/
A1  - Bui, Hong Nhung
A1  - Vu, Trong-Sinh
A1  - Nguyen, Tri Thanh
A1  - Nguyen, Thi Cham
A1  - Ha, Quang Thuy
Y1  - 2019/10/24/
N2  - ? 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.
TI  - A Compact Trace Representation Using Deep Neural Networks for Process Mining
M2  - Da Nang, Vietnam
AV  - none
T2  - The 11th IEEE International Conference on Knowledge and Systems Engineering
ER  -