Getting Things in Order: An Introduction to the R Package seriation

By Michael Hahsler, Kurt Hornik, Christian Buchta. 2008

409 citations recorded by the source corpus.

Seriation, i.e., finding a suitable linear order for a set of objects given data and a loss or merit function, is a basic problem in data analysis. Caused by the problem's combinatorial nature, it is hard to solve for all but very small sets. Nevertheless, both exact solution methods and heuristics are available. In this paper we present the package seriation which provides an infrastructure for seriation with R. The infrastructure comprises data structures to represent linear orders as permutation vectors, a wide array of seriation methods using a consistent interface, a method to calculate the value of various loss and merit functions, and several visualization techniques which build on seriation. To illustrate how easily the package can be applied for a variety of applications, a comprehensive collection of examples is presented.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/3ccf052a90c6ac47e4274f98acc4d59223dba996

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zuguang Gu
2. D. Huebschmann

In Information Systems

1. D. Huebschmann
2. Zuguang Gu

Citations in the corpus

• Complex heatmap visualization (2022, background): “Examples of popular packages for ordering matrices are seriation [23] and biclust [24].”
• Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization (2011, neutral): “[23], deserves deeper investigation and acknowledgment.”
• Make Interactive Complex Heatmaps in R (2021, background): “…procedures, matrix for heatmap visualization is normally accompanied with row and column reordering, e.g. , by hierarchical clustering or seriation (Hahsler, Hornik, and Buchta 2008), so that features with similar patterns are grouped closely and they can be easily identified from the colors on…”