Occupation Clustering Using Deep Embedded Kmeans

DOI

10.2991/978-94-6463-583-6_2How to use a DOI?

Keywords

Occupation Clustering; Deep Embedded Clustering; Deep Embedded KMeans Clustering; KMeans

Abstract

While applications of clustering in business commonly focus on business products or geographic concentrations, occupation clustering concentrates on the knowledge, skills, interests, and abilities of employees. Occupation clustering analysis provides more insights into the talent, knowledge, and skills of employees than the education level and qualifications of employees. To gain insights into occupation, this paper proposes an occupation clustering based on Deep Embedded KMeans clustering. Our occupation clustering works on three factors: interests, knowledge and skills of each occupation according to knowledge classification and measurement system. In the experimental result, we have twelve clusters of occupation with interests, knowledge, and skills. The result can help us to gain deeper insights into the skills, interests, and knowledge of employees.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ngoc-Toan Le
AU  - Thanh-Hieu Bui
PY  - 2024
DA  - 2024/11/26
TI  - Occupation Clustering Using Deep Embedded Kmeans
BT  - Proceedings of the 2nd International Conference - Resilience by Technology and Design (RTD 2024)
PB  - Atlantis Press
SP  - 7
EP  - 16
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-583-6_2
DO  - 10.2991/978-94-6463-583-6_2
ID  - Le2024
ER  -