Clustering: an R library to facilitate the analysis and comparison of cluster algorithms

Abstract

Clustering is an unsupervised learning method that divides data into groups of similar features. Researchers use this technique to categorise and automatically classify unlabelled data to reveal data concentrations. Although there are other implementations of clustering algorithms in R, this paper introduces the Clustering library for R, aimed at facilitating the analysis and comparison between clustering algorithms. Specifically, the library uses relevant clustering algorithms from the literature with two objectives: firstly to group data homogeneously by establishing differences between clusters and secondly to generate a ranking between the algorithms and the attributes of a data set to obtain the optimal number of clusters. Finally, it is crucial to highlight the added value that the library provides through its interactive graphical user interface, where experiments can be easily configured and executed without requiring expert knowledge of the parameters of each algorithm.