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COPS: Cluster optimized proximity scaling

Rusch, Thomas and Mair, Patrick and Hornik, Kurt ORCID: (2015) COPS: Cluster optimized proximity scaling. Discussion Paper Series / Center for Empirical Research Methods, 2015/1. WU Vienna University of Economics and Business, Vienna.

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Proximity scaling methods (e.g., multidimensional scaling) represent objects in a low dimensional configuration so that fitted distances between objects optimally approximate multivariate proximities. Next to finding the optimal configuration the goal is often also to assess groups of objects from the configuration. This can be difficult if the optimal configuration lacks clusteredness (coined c-clusteredness). We present Cluster Optimized Proximity Scaling (COPS), which attempts to solve this problem by finding a configuration that exhibts c-clusteredness. In COPS, a flexible scaling loss function (p-stress) is combined with an index that quantifies c-clusteredness in the solution, the OPTICS Cordillera. We present two variants of combining p-stress and Cordillera, one for finding the configuration directly and one for metaparameter selection for p-stress. The first variant is illustrated by scaling Californian counties with respect to climate change related natural hazards. We identify groups of counties with similar risk profiles and find that counties that are in high risk of drought are socially vulnerable. The second variant is illustrated by finding a clustered nonlinear representation of countries according to their history of banking crises from 1800 to 2010. (authors' abstract)

Item Type: Paper
Keywords: multidimensional scaling / nonlinear dimension reduction / clusteredness / data visualization / exploratory data analysis
Depositing User: ePub Administrator
Date Deposited: 26 Feb 2016 11:53
Last Modified: 24 Oct 2019 13:41

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