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) / Series: Discussion Paper Series / Center for Empirical Research Methods
| Identifer | oai:union.ndltd.org:VIENNA/oai:epub.wu-wien.ac.at:4888 |
| Date | January 2015 |
| Creators | Rusch, Thomas, Mair, Patrick, Hornik, Kurt |
| Publisher | WU Vienna University of Economics and Business |
| Source Sets | Wirtschaftsuniversität Wien |
| Language | English |
| Detected Language | English |
| Type | Paper, NonPeerReviewed |
| Format | application/pdf |
| Relation | http://epub.wu.ac.at/4888/ |
Page generated in 0.0019 seconds