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Data Clustering with Actuarial Applications

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  • Guojun Gan
  • Emiliano A. Valdez

Abstract

Data clustering refers to the process of dividing a set of objects into homogeneous groups or clusters such that the objects in each cluster are more similar to each other than to those of other clusters. As one of the most popular tools for exploratory data analysis, data clustering has been applied in many scientific areas. In this article, we give a review of the basics of data clustering, such as distance measures and cluster validity, and different types of clustering algorithms. We also demonstrate the applications of data clustering in insurance by using two scalable clustering algorithms, the truncated fuzzy c-means (TFCM) algorithm and the hierarchical k-means algorithm, to select representative variable annuity contracts, which are used to build predictive models. We found that the hierarchical k-means algorithm is efficient and produces high-quality representative variable annuity contracts.

Suggested Citation

  • Guojun Gan & Emiliano A. Valdez, 2020. "Data Clustering with Actuarial Applications," North American Actuarial Journal, Taylor & Francis Journals, vol. 24(2), pages 168-186, April.
    • Handle: RePEc:taf:uaajxx:v:24:y:2020:i:2:p:168-186
    DOI: 10.1080/10920277.2019.1575242
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    Cited by:

    1. Shengkun Xie & Kun Shi, 2023. "Generalised Additive Modelling of Auto Insurance Data with Territory Design: A Rate Regulation Perspective," Mathematics, MDPI, vol. 11(2), pages 1-24, January.
    2. Jiang, Ruihong & Saunders, David & Weng, Chengguo, 2023. "Two-phase selection of representative contracts for valuation of large variable annuity portfolios," Insurance: Mathematics and Economics, Elsevier, vol. 113(C), pages 293-309.
    3. Jamotton, Charlotte & Hainaut, Donatien, 2024. "Latent Dirichlet Allocation for structured insurance data," LIDAM Discussion Papers ISBA 2024008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Charlotte Jamotton & Donatien Hainaut & Thomas Hames, 2024. "Insurance Analytics with Clustering Techniques," Risks, MDPI, vol. 12(9), pages 1-28, September.
    5. Nicholas Bett & Juma Kasozi & Daniel Ruturwa, 2022. "Temporal Clustering of the Causes of Death for Mortality Modelling," Risks, MDPI, vol. 10(5), pages 1-34, May.
    6. Shuang Yin & Guojun Gan & Emiliano A. Valdez & Jeyaraj Vadiveloo, 2021. "Applications of Clustering with Mixed Type Data in Life Insurance," Risks, MDPI, vol. 9(3), pages 1-19, March.

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