Title
Cluster Analysis in R with Big Data Applications
Document Type
Book Chapter
Keywords
cluster analysis; data analytics
Identifier Data
https://doi.org/10.4018/978-1-7998-2768-9.ch004
Publisher
IGI Global
Publication Source
Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities
Abstract
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).
Comments
Book chapter in Open Source Software for Statistical Analysis of Big Data: Research and Opportunities.