利用聚类技术进行保险分析

IF 2 Q2 BUSINESS, FINANCE
Risks Pub Date : 2024-09-05 DOI:10.3390/risks12090141
Charlotte Jamotton, Donatien Hainaut, Thomas Hames
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引用次数: 0

摘要

K-means 算法及其变体是著名的聚类技术。在精算应用中,这些分区方法可以识别具有相似属性的保单群组。由此产生的分区为创建主要风险地图和无监督定价网格提供了精算框架。本研究文章旨在将成熟的聚类方法应用于包含分类变量和数字变量的复杂保险数据集。为此,我们提出了一种基于伯特距离的新方法。我们首先回顾了 K-means 算法,为我们基于伯特距离的框架奠定了基础。接下来,我们将迷你批次和模糊 K-means 变体的应用范围扩展到异构保险数据。此外,我们还采用了光谱聚类技术,这是一种基于图论的技术,可适应非凸聚类形状。为了减轻光谱聚类的 O(n3) 运行时间所带来的计算复杂性,我们采用基于伯特距离的方法,为大规模数据集引入了一种数据缩减方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Insurance Analytics with Clustering Techniques
The K-means algorithm and its variants are well-known clustering techniques. In actuarial applications, these partitioning methods can identify clusters of policies with similar attributes. The resulting partitions provide an actuarial framework for creating maps of dominant risks and unsupervised pricing grids. This research article aims to adapt well-established clustering methods to complex insurance datasets containing both categorical and numerical variables. To achieve this, we propose a novel approach based on Burt distance. We begin by reviewing the K-means algorithm to establish the foundation for our Burt distance-based framework. Next, we extend the scope of application of the mini-batch and fuzzy K-means variants to heterogeneous insurance data. Additionally, we adapt spectral clustering, a technique based on graph theory that accommodates non-convex cluster shapes. To mitigate the computational complexity associated with spectral clustering’s O(n3) runtime, we introduce a data reduction method for large-scale datasets using our Burt distance-based approach.
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来源期刊
Risks
Risks Economics, Econometrics and Finance-Economics, Econometrics and Finance (miscellaneous)
CiteScore
3.80
自引率
22.70%
发文量
205
审稿时长
11 weeks
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