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Authors: Shengkun Xie 1 ; Anna T. Lawniczak 2 and Zizhen Wang 2

Affiliations: 1 University of Toronto Mississauga and Ryerson University, Canada ; 2 University of Guelph, Canada

Keyword(s): Spatially Constrained Clustering, Ratemaking, Geocoding, Gap Statistic, Business Data Analytic, Model Selection.

Related Ontology Subjects/Areas/Topics: Applications ; Clustering ; Economics, Business and Forecasting Applications ; Model Selection ; Pattern Recognition ; Theory and Methods

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.

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Paper citation in several formats:
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 - ICPRAM; ISBN 978-989-758-222-6; ISSN 2184-4313, SciTePress, pages 82-88. DOI: 10.5220/0006118100820088

@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 - ICPRAM},
year={2017},
pages={82-88},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006118100820088},
isbn={978-989-758-222-6},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Spatially Constrained Clustering to Define Geographical Rating Territories
SN - 978-989-758-222-6
IS - 2184-4313
AU - Xie, S.
AU - Lawniczak, A.
AU - Wang, Z.
PY - 2017
SP - 82
EP - 88
DO - 10.5220/0006118100820088
PB - SciTePress