Talk:Hierarchical clustering

This is the talk page for discussing improvements to the Hierarchical clustering article.
This is not a forum for general discussion of the article's subject.

Put new text under old text. Click here to start a new topic.
New to Wikipedia? Welcome! Learn to edit; get help.

Article policies

Find sources: Google (books · news · scholar · free images · WP refs) · FENS · JSTOR · TWL

Statistics Low‑importance

	This article is within the scope of WikiProject Statistics, a collaborative effort to improve the coverage of statistics on Wikipedia. If you would like to participate, please visit the project page, where you can join the discussion and see a list of open tasks.StatisticsWikipedia:WikiProject StatisticsTemplate:WikiProject StatisticsStatistics
Low	This article has been rated as Low-importance on the importance scale.

Computer science Mid‑importance

This article is within the scope of WikiProject Computer science, a collaborative effort to improve the coverage of Computer science related articles on Wikipedia. If you would like to participate, please visit the project page, where you can join the discussion and see a list of open tasks.Computer scienceWikipedia:WikiProject Computer scienceTemplate:WikiProject Computer scienceComputer science

Mid

This article has been rated as Mid-importance on the project's importance scale.

Things you can help WikiProject Computer science with:

Here are some tasks awaiting attention:

Article requests :
- Requested articles/Applied arts and sciences/Computer science, computing, and Internet
Cleanup :
- Computer science articles needing attention
- Computer science articles needing expert attention
Copyedit :
- Computing
Expand :
- Computer science
Infobox :
- Computer science articles without infoboxes
Maintain :
- Timeline of computing 2020–present
Photo :
- Find pictures for the biographies of computer scientists (see List of computer scientists)
- Computing articles needing images
Stubs :
- Computer science stubs
Unreferenced :
- WikiProject Computer science/Unreferenced BLPs
Project-related :
- Tag all relevant articles in Category:Computer science and sub-categories with {{WikiProject Computer science}}

Databases (inactive)

This article is within the scope of WikiProject Databases, a project which is currently considered to be inactive.DatabasesWikipedia:WikiProject DatabasesTemplate:WikiProject DatabasesDatabases

Text and/or other creative content from Cluster analysis was copied or moved into Hierarchical clustering with this edit. The former page's history now serves to provide attribution for that content in the latter page, and it must not be deleted as long as the latter page exists.

This is the "main article" for Hierarchical clustering according to the Cluster_analysis page, yet that page actually has more information than this one about Hierarchical clustering algorithms. Surely this should not be. . . Electron100 (talk) 03:05, 21 September 2009 (UTC)[reply]

While I agree that this article is seriously lacking information I believe this page needs to be enhanced rather than merged. Using K-means clustering as an example the cluster analysis page gives an overview, but the main article provides more detailed information. (Humanpowered (talk) 15:30, 23 March 2011 (UTC))[reply]

Added WikiLink to User:Mathstat/Ward's_method — Preceding unsigned comment added by Jmajf (talk • contribs) 12:49, 28 November 2011 (UTC)[reply]

give example

Dear Sir Please write fluent and understandable about several kind of hierarchical clustering and please give example. — Preceding unsigned comment added by 83.172.123.165 (talk) 19:04, 16 December 2011 (UTC)[reply]

In the section "Metric" am I right that the "i" across which some of the distance metrics are summed is an index of data dimension? i.e. bivariate data will be i = {1, 2}.

If so it might make it clearer to put this definition of i in the text to make it clear to simpletons like me! Also two of the measures (Mahalanobis and cosine) do not sum across i. Does this mean they can only be used for single variate data? If not, is there another formula?