COREclust: a new package for a robust and scalable analysis of complex data
This provides a robust and scalable tool for researchers analyzing complex, high-dimensional datasets, though it is incremental as it builds on existing clustering methods.
The authors tackled the problem of detecting representative variables in high-dimensional data with limited observations by introducing the COREclust R package, which uses a graph clustering algorithm to identify variable sets and robustly estimate their centers, demonstrating effectiveness on synthetic and real data.
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.