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Data Clustering with Quantum Mechanics

Author

Listed:
  • Tony C. Scott

    (College of Physics and Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, China
    Near India Pvt Ltd., No. 71/72, Jyoti Nivas College Road, Koramangala, Bengalore 560095, India
    These authors contributed equally to this work.)

  • Madhusudan Therani

    (Near India Pvt Ltd., No. 71/72, Jyoti Nivas College Road, Koramangala, Bengalore 560095, India
    EngKraft LLC, 312 Adeline Avenue, San Jose, CA 95136, USA
    These authors contributed equally to this work.)

  • Xing M. Wang

    (Sherman Visual Lab, Sunnyvale, CA 94085, USA
    These authors contributed equally to this work.)

Abstract

Data clustering is a vital tool for data analysis. This work shows that some existing useful methods in data clustering are actually based on quantum mechanics and can be assembled into a powerful and accurate data clustering method where the efficiency of computational quantum chemistry eigenvalue methods is therefore applicable. These methods can be applied to scientific data, engineering data and even text.

Suggested Citation

  • Tony C. Scott & Madhusudan Therani & Xing M. Wang, 2017. "Data Clustering with Quantum Mechanics," Mathematics, MDPI, vol. 5(1), pages 1-17, January.
    • Handle: RePEc:gam:jmathe:v:5:y:2017:i:1:p:5-:d:87032
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    References listed on IDEAS

    as
    1. Jing Chen, 2005. "The Physical Foundation of Economics:An Analytical Thermodynamic Theory," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 5819.
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    Cited by:

    1. Stan Lipovetsky, 2023. "Quantum-like Data Modeling in Applied Sciences: Review," Stats, MDPI, vol. 6(1), pages 1-9, February.
    2. Wang, Cong & Chen, Yunxia & Zhang, Qingyuan & Zhu, Jiaxiao, 2023. "Dynamic early recognition of abnormal lithium-ion batteries before capacity drops using self-adaptive quantum clustering," Applied Energy, Elsevier, vol. 336(C).

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