Spatially Constrained Clustering to Define Geographical Rating Territories

Shengkun Xie, Anna T. Lawniczak, Zizhen Wang

2017

Abstract

In this work, spatially constrained clustering of insurance loss cost is studied. The study has demonstrated that spatially constrained clustering is a promising technique for defining geographical rating territories using auto insurance loss data as it is able to satisfy the contiguity constraint while implementing clustering. In the presented work, to ensure statistically sound clustering, advanced statistical approaches, including average silhouette statistic and Gap statistic, were used to determine the number of clusters. The proposed method can also be applied to demographical data analysis and real estate data clustering due to the nature of spatial constraint.

Paper Citation

in Harvard Style

Xie S., Lawniczak A. and Wang Z. (2017). Spatially Constrained Clustering to Define Geographical Rating Territories . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 82-88. DOI: 10.5220/0006118100820088

in Bibtex Style

@conference{icpram17,
author={Shengkun Xie and Anna T. Lawniczak and Zizhen Wang},
title={Spatially Constrained Clustering to Define Geographical Rating Territories},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={82-88},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006118100820088},
isbn={978-989-758-222-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Spatially Constrained Clustering to Define Geographical Rating Territories
SN - 978-989-758-222-6
AU - Xie S.
AU - Lawniczak A.
AU - Wang Z.
PY - 2017
SP - 82
EP - 88
DO - 10.5220/0006118100820088