Truecluster: robust scalable clustering with model selection
  Data-based classification is fundamental to most branches of science. While
recent years have brought enormous progress in various areas of statistical
computing and clustering, some general challenges in clustering remain: model
selection, robustness, and scalability to large datasets. We consider the
important problem of deciding on the optimal number of clusters, given an
arbitrary definition of space and clusteriness. We show how to construct a
cluster information criterion that allows objective model selection. Differing
from other approaches, our truecluster method does not require specific
assumptions about underlying distributions, dissimilarity definitions or
cluster models. Truecluster puts arbitrary clustering algorithms into a generic
unified (sampling-based) statistical framework. It is scalable to big datasets
and provides robust cluster assignments and case-wise diagnostics. Truecluster
will make clustering more objective, allows for automation, and will save time
and costs. Free R software is available.
