Linear Storage and Potentially Constant Time Hierarchical Clustering Using the Baire Metric and Random Spinning Paths

Fionn Murtagh, Pedro Contreras

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


We study how random projections can be used with large data sets in order (1) to cluster the data using a fast, binning approach which is characterized in terms of direct inducing of a hierarchy through use of the Bairemetric; and (2) based on clusters found, selecting subsets of the original data for further analysis. In this work, we focus on random projection that is used for processing high dimensional data. A random projection, outputting a random permutation of the observation set, provides a random spanning path. We show how a spanning path relates to contiguity- or adjacency-constrained clustering.We study performance properties of hierarchical clustering constructed from random spanning paths, and we introduce a novel visualization of the results.

Original languageEnglish
Title of host publicationAnalysis of Large and Complex Data
PublisherKluwer Academic Publishers
Number of pages10
ISBN (Print)9783319252247
Publication statusPublished - 4 Aug 2016
Externally publishedYes
Event2nd European Conference on Data Analysis - Bremen, Germany
Duration: 2 Jul 20144 Jul 2014
Conference number: 2


Conference2nd European Conference on Data Analysis
Abbreviated titleECDA 2014


Cite this