The leader clustering algorithm provides a means for clustering a set of data points. Unlike many other clustering algorithms it does not require the user to specify the number of clusters, but instead requires the approximate radius of a cluster as its primary tuning parameter. The package provides a fast implementation of this algorithm in n-dimensions using Lp-distances (with special cases for p=1,2, and infinity) as well as for spatial data using the Haversine formula, which takes latitude/longitude pairs as inputs and clusters based on great circle distances.
| Version: | 1.2 |
| Published: | 2014-12-16 |
| Author: | Taylor B. Arnold |
| Maintainer: | Taylor B. Arnold <taylor.arnold at acm.org> |
| License: | LGPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | leaderCluster results |
| Reference manual: | leaderCluster.pdf |
| Package source: | leaderCluster_1.2.tar.gz |
| Windows binaries: | r-devel: leaderCluster_1.2.zip, r-release: leaderCluster_1.2.zip, r-oldrel: leaderCluster_1.2.zip |
| macOS binaries: | r-release: leaderCluster_1.2.tgz, r-oldrel: leaderCluster_1.2.tgz |
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