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Data-Driven Organizational Structure Optimization: Variable-Scale Clustering

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LISS 2020

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Abstract

With the continuous improvement of external data acquisition ability and computing power, data-driven optimization of organizational structure becomes an emerging technique for various enterprises to develop business performance and control management costs. This paper focuses on the management scale level discovery problem for the optimization of enterprise organizational structure. Firstly, according to the scale transformation theory, the scale level of the multi-scale dataset is defined. Then, a scale level discovery method based on the variable-scale clustering (SLD-VSC) is proposed. After determining management objectives, the SLD-VSC is able to recognize optimal management scale level and the scale characteristics of each management object clusters distributed in different management scale levels. The numerical experimental results illustrate that the proposed SLD-VSC is able to support enterprises improving their organizational structure by identifying the management scale levels from business data.

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References

  1. X. Gao, A. Wang, Variable-scale clustering, in Proceeding of the 8th International Conference on Logistics, Informatics and Service Sciences (Toronto, Canada, 2018), pp. 221–225

    Google Scholar 

  2. X. Gao, A. Wang, Customer satisfaction analysis and management method based on enterprise network public opinion, in Operations Research and Management Science (In Press, 2019)

    Google Scholar 

  3. A. Wang, X. Gao, Multifunctional product marketing using social media based on the variable-scale clustering. Tech. Gaz. 26(1), 193–200 (2019)

    Google Scholar 

  4. A. Wang, X. Gao, Hybrid variable-scale clustering method for social media marketing on user generated instant music video. Tech. Gaz. 26(3), 771–777 (2019)

    Google Scholar 

  5. A. Wang, X. Gao, M. Yang, Variable-scale clustering based on the numerical concept space, in Proceeding of the 9th International Conference on Logistics, Informatics and Service Sciences (Maryland. US) (2019), pp. 65–69

    Google Scholar 

  6. S. Wu, X. Gao, M.M. Bastien, Data Warehousing and Data Mining (Metallurgical Industry Press, China, 2003), pp. 148–155

    Google Scholar 

  7. A. Wang, X. Gao, Technique of data mining tasks discovery for data mining, in Proceeding of the 7th International Conference on Logistics, Informatics and Service Sciences, Kyoto. Japan (In Press, 2017)

    Google Scholar 

  8. A. Wang, X. Gao, Multi-tasks discovery method based on the concept network for data mining. IEEE Access, 7, 139537–139547 (2019)

    Google Scholar 

  9. X. Chen, Technology of Thinking Processes Discovery for Data Mining Application (University of Science and Technology Beijing, Beijing, 2012)

    Google Scholar 

  10. K. Gu, Technology of Concept Pair Identification for Thinking Theme Discovery (University of Science and Technology Beijing, Beijing, 2013)

    Google Scholar 

  11. A. Wang, X. Gao, M. Tang, Computer supported data-driven decisions for service personalization: a variable-scale clustering method. Stud. Inf. Cont. 29(1), 55–65 (2020)

    Google Scholar 

  12. L.L. Qin, N.W. Yu, D.H. Zhao, Applying the convolutional neural network deep learning technology to behavioural recognition in intelligent video. Tehnicki Vjesnik-Tech Gaz 25(2), 528–535 (2018)

    Google Scholar 

  13. A. Wang, X. Gao, Intelligent computing: knowledge acquisition method based on the management scale transformation. Comput. J. (2020) (In Press)

    Google Scholar 

  14. J. Li, S.X. Pan, L. Huang, X. Zhu, A machine learning based method for customer behavior prediction. Tehnicki Vjesnik-Tech. Gaz. 26(6), 1670–1676 (2019)

    Google Scholar 

  15. L.M. Wang, Z.Y. Hao, X.M. Han, R.H. Zhou, Gravity theory-based affinity propagation clustering algorithm and its applications. Tehnicki vjesnik-Tech. Gaz. 25(4), 1125–1135 (2018)

    Google Scholar 

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Authors and Affiliations

  1. School of Economics and Management, University of Science and Technology Beijing, Beijing, China

    Ai Wang & Xuedong Gao

Authors
  1. Ai Wang
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  2. Xuedong Gao
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Corresponding author

Correspondence to Xuedong Gao .

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Editors and Affiliations

  1. Beijing Jiaotong University, Beijing, China

    Shifeng Liu

  2. Budapest University of Technology and Economics, Budapest, Hungary

    Gábor Bohács

  3. Beijing Jiaotong University, Beijing, China

    Xianliang Shi

  4. International Center for Informatics Research, Beijing Jiaotong University, Beijing, China

    Xiaopu Shang

  5. Beijing Logistics Informatics Research Base, Beijing Jiaotong University, Beijing, China

    Anqiang Huang

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Wang, A., Gao, X. (2021). Data-Driven Organizational Structure Optimization: Variable-Scale Clustering. In: Liu, S., Bohács, G., Shi, X., Shang, X., Huang, A. (eds) LISS 2020. Springer, Singapore. https://doi.org/10.1007/978-981-33-4359-7_6

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