Methods of Hierarchical Clustering. / Murtagh, Fionn; Contreras, Pedro.

Data Mining and Knowledge Discovery. Wiley-Interscience, 2011.

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Abstract

We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm.
Original languageUndefined/Unknown
Title of host publicationData Mining and Knowledge Discovery
PublisherWiley-Interscience
Publication statusPublished - 2011
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 1819939