Cluster Analysis in R with Big Data Applications

Document Type

Book Chapter


Book chapter in Open Source Software for Statistical Analysis of Big Data: Research and Opportunities.


cluster analysis; data analytics

Identifier Data



IGI Global

Publication Source

Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities


This chapter discusses several popular clustering functions and open source software packages in R and their feasibility of use on larger datasets. These will include the kmeans() function, the pvclust package, and the DBSCAN (density-based spatial clustering of applications with noise) package, which implement K-means, hierarchical, and density-based clustering, respectively. Dimension reduction methods such as PCA (principle component analysis) and SVD (singular value decomposition), as well as the choice of distance measure, are explored as methods to improve the performance of hierarchical and model-based clustering methods on larger datasets. These methods are illustrated through an application to a dataset of RNA-sequencing expression data for cancer patients obtained from the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) data collection from The Cancer Imaging Archive (TCIA).