Hierarchical clustering: Difference between revisions

Revision as of 05:08, 11 November 2024

Revision as of 07:23, 11 November 2024

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Hierarchical clustering has the distinct advantage that any valid measure of distance can be used. In fact, the observations themselves are not required: all that is used is a [[distance matrix|matrix of distances]]. On the other hand, except for the special case of single-linkage distance, none of the algorithms (except exhaustive search in <math>\mathcal{O}(2^n)</math>) can be guaranteed to find the optimum solution.{{cn|date=October 2024}}

An example of modern application would be the formalisation of abstract [[cryptocurrencies]] sectors using [[web-scrapping]] <ref name="pm-research.com">{{cite journal|author=Mahdavi Damghani B., Fraser R., Howell J., Halldorsson S. |title=Cryptocurrency Sectorization through Clustering and Web-Scraping: Application to Systematic Trading|journal=The Journal of Financial Data Science|volume=4|issue=1|pages=158 - 179|year=2022|doi=10.3905/jfds.2021.1.080}}</ref>.

== Complexity ==