Hierarchical clustering

Pedro Contreras, Fionn Murtagh

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

10 Citations (Scopus)

Abstract

To begin with, we review dissimilarity, metric, and ultrametric. Next, we introduce hierarchical clustering using the single link agglomerative criterion. Then we present agglomerative hierarchical clustering in full generality. Storage and computational properties are reviewed. This includes the state-of-the art agglomerative hierarchical clustering algorithm that uses a nearest-neighbor chain and reciprocal nearest neighbors. We then review various, recently developed, hierarchical clustering algorithms that use density or grid-based approaches. That includes a linear time algorithm. A number of examples, and R implementation, completes this chapter.

Original languageEnglish
Title of host publicationHandbook of Cluster Analysis
EditorsChristian Hennig, Marina Meila, Fionn Murtagh, Roberto Rocci
PublisherCRC Press
Pages103-124
Number of pages22
ISBN (Electronic)9781466551893
ISBN (Print)9781466551886
DOIs
Publication statusPublished - 1 Dec 2015
Externally publishedYes

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  • Handbook of Cluster Analysis

    Hennig, C. (ed.), Meila, M. (ed.), Murtagh, F. (ed.) & Rocci, R. (ed.), 1 Dec 2015, CRC Press. 773 p. (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)

    Research output: Book/ReportBookpeer-review

    123 Citations (Scopus)

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