COREclust: a new package for a robust and scalable analysis of complex data

Camille Champion (1) , Anne-Claire Brunet (1) , Jean-Michel Loubes (1) , Laurent Risser (1)

1 IMT - Institut de Mathématiques de Toulouse UMR5219

In this paper, we present a new R package COREclust dedicated to the detection of representative variables in high dimensional spaces with a potentially limited number of observations. Variable sets detection is based on an original graph clustering strategy denoted CORE-clustering algorithm that detects CORE-clusters, i.e. variable sets having a user defined size range and in which each variable is very similar to at least another variable. Representative variables are then robustely estimate as the CORE-cluster centers. This strategy is entirely coded in C++ and wrapped by R using the Rcpp package. A particular effort has been dedicated to keep its algorithmic cost reasonable so that it can be used on large datasets. After motivating our work, we will explain the CORE-clustering algorithm as well as a greedy extension of this algorithm. We will then present how to use it and results obtained on synthetic and real data.

Domaines: Logiciel mathématique [cs.MS]; Machine Learning [stat.ML]; Statistiques [math.ST]; Calcul [stat.CO]

Camille Champion, Anne-Claire Brunet, Jean-Michel Loubes, Laurent Risser. COREclust: a new package for a robust and scalable analysis of complex data. 2018. ⟨hal-01799117⟩