A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2012; you can also visit the original URL.
The file type is application/pdf
.
GAC-GEO: a generic agglomerative clustering framework for geo-referenced datasets
2010
Knowledge and Information Systems
Major challenges of clustering geo-referenced data include identifying arbitrarily shaped clusters, properly utilizing spatial information, coping with diverse extrinsic characteristics of clusters and supporting region discovery tasks. The goal of region discovery is to identify interesting regions in geo-referenced datasets based on a domain expert's notion of interestingness. Almost all agglomerative clustering algorithms only focus on the first challenge. The goal of the proposed work is to
doi:10.1007/s10115-010-0355-3
fatcat:agwlvb7wffeczk5fd2lojknedq