optimus - Model Based Diagnostics for Multivariate Cluster Analysis

Assessment and diagnostics for comparing competing clustering solutions, using predictive models. The main intended use is for comparing clustering/classification solutions of ecological data (e.g. presence/absence, counts, ordinal scores) to 1) find an optimal partitioning solution, 2) identify characteristic species and 3) refine a classification by merging clusters that increase predictive performance. However, in a more general sense, this package can do the above for any set of clustering solutions for i observations of j variables.

Last updated 7 years ago

3.78 score 1 stars 12 scripts 171 downloads

c2c - Compare Two Classifications or Clustering Solutions of Varying Structure

Compare two classifications or clustering solutions that may or may not have the same number of classes, and that might have hard or soft (fuzzy, probabilistic) membership. Calculate various metrics to assess how the clusters compare to each other. The calculations are simple, but provide a handy tool for users unfamiliar with matrix multiplication. This package is not geared towards traditional accuracy assessment for classification/ mapping applications - the motivating use case is for comparing a probabilistic clustering solution to a set of reference or existing class labels that could have any number of classes (that is, without having to degrade the probabilistic clustering to hard classes).

Last updated 8 years ago

3.70 score 7 scripts 213 downloads