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

TL;DR

COREclust is an R package that uses a novel graph clustering algorithm to identify representative variable sets in high-dimensional data, providing robust estimates even with limited observations.

Contribution

The paper introduces COREclust, a new scalable R package with a graph clustering algorithm for robust variable selection in high-dimensional, small-sample datasets.

Findings

Effective detection of variable sets in synthetic data.

Application to real datasets demonstrates robustness.

Algorithm maintains reasonable computational cost.

Abstract

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.