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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? — Preceding unsigned comment added by Periololon (talk • contribs) 14:44, 19 March 2012 (UTC)[reply]

V-linkage V-means

I was interested in this technique but I haven't found any reference, searching Google, Google Scholar. We need a source/reference.Moo (talk) 20:25, 11 May 2012 (UTC)[reply]

I found the following on what appears to be an old copy of the artcle cluster analysis at http://biocomp.bioen.uiuc.edu/oscar/tools/Hierarchical_Clustering.html
V-means clustering
V-means clustering utilizes cluster analysis and nonparametric statistical tests to key researchers into segments of data that may contain distinct homogenous sub-sets. The methodology embraced by V-means clustering circumvents many of the problems that traditionally beleaguer standard techniques for categorizing data. First, instead of relying on analyst predictions for the number of distinct sub-sets (k-means clustering), V-means clustering generates a pareto optimal number of sub-sets. V-means clustering is calibrated to a user-defined confidence level p, whereby the algorithm divides the data and then recombines the resulting groups until the probability that any given group belongs to the same distribution as either of its neighbors is less than p.
Second, V-means clustering makes use of repeated iterations of the nonparametric Kolmogorov-Smirnov test. Standard methods of dividing data into its constituent parts are often entangled in definitions of distances (distance measure clustering) or in assumptions about the normality of the data (expectation maximization clustering), but nonparametric analysis draws inference from the distribution functions of sets.
Third, the method is conceptually simple. Some methods combine multiple techniques in sequence in order to produce more robust results. From a practical standpoint this muddles the meaning of the results and frequently leads to conclusions typical of “data dredging.”
Unfortunately there was no citation. Melcombe (talk) 22:27, 11 May 2012 (UTC)[reply]

Hierarchical Clustering References

There is a 1967 paper, published in Psychometrika, titled "Hierarchical Clustering Schemes", by S. C. Johnson (yes, that's me...). It was extensively cited in the 70's and 80's, in part because Bell Labs gave away a FORTRAN program for free that did a couple of the methods described in the paper. The paper pointed out that there is a correspondence between hierarical clusterings and a kind of data metric called an ultrametric -- whenever you have a hierarchical clustering, it implies an ultrametic, and conversely. 76.244.36.165 (talk) 19:14, 18 October 2012 (UTC) Stephen C Johnson[reply]

US Patent application 14/718,804 achieves sub-quadratic complexity for dissimilarity measures based on distances in a Euclidean vector space.

http://arxiv.org/abs/1109.2378 is a good survey of the algorithms. — Preceding unsigned comment added by 2001:4898:80E8:B:5A:FC6F:C36B:3C4C (talk) 00:44, 4 October 2018 (UTC)[reply]

Example for Agglomerative Clustering edit

I changed The "increase" in variance for the cluster being merged (Ward's method[7]) to The "decrease" in variance for the cluster being merged (Ward's method[7]). So it is also above, to Cluster dissimilarity and so appears from Ward's method, https://en.wikipedia.org/wiki/Ward%27s_method 

 — Preceding unsigned comment added by 2A02:5D8:200:600:82:150:200:4 (talk) 11:44, 20 August 2015 (UTC)[reply]

Divisive algorithms, hierarchical k-means

I think that hierarchical k-means deserves a mention or description, maybe even it's own page. As a starting point I'm mentioning it here. Perhaps the way to go is Hierarchical clustering#(agglomerative methods#(...),divisive#(hierarchical-kmeans,..others..)). Someone already tried to delete my reference to hkmeans in means saying it was spam - I think that's a little unfair, so i'm trying to explain it better. Fmadd (talk) 06:45, 14 May 2016 (UTC)[reply]

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Based on the rough observation of the change logs, the summary table of the 17 linkage criteria has been added on around 2014. Now, it has been reduced to 16 linkage criteria in such a way with a potential Wiki logic problem…

Based on the rough observation of the change logs, the summary table of the 17 linkage criteria has been added on around 2014. On April 26-27, 2024, as there are only 6 linkage criteria with their own Wiki pages. An effort, very new to the Wiki edit requirements, has been made to add a Wiki pages to a linkage criteria. Instead of just deleting the link to this new Wiki page, the existing linkage criteria has been deleted by the Wiki editor as well. Thus, the summary table constructed in 2014 of the 17 linkage criteria has now become 16 linkage criteria.

The main problem of over-deleting the record is that this can be abused by others intentionally to eliminate their competitors, and have the potential to cause serious loss of information for Wiki. Assume that there are three commercial products, A, B, and C, for a biological test experiment listed in Wiki. Supposed that the product A wants to eliminate products B and C in the Wiki list. Simply, product A can do cite-spamming for products B and C. The Wiki Editor will easily notice that there are cite-spamming for products B and C. Thus, in according with the Wiki practice for this page, the Wiki Editor will delete the Products B and C from Wiki. As a result, Product A will become the sole product listed in Wiki for that biological test experiment.

The above imaged case is to highlight that existing records before any possible cite-spamming should not be affected, and should be kept intact. Otherwise, old records as old as ten years can be easily deleted, and people of bad intention may just abuse the Wiki system in another way by using the Wiki Editor to delete their possible competitors in the Wiki.

Looking forward to the attention by the Wiki people who really care about how to do the best practice for the Wiki. It is understood that each organization has its own practices, and someone may not fully understand all these rules very well and thus overlook some rules. Yet, the consequence of over-deleting may become even much more serious than those guys who are just starting the learning process for the Wiki edit functions and still yet to understand all its many rules. 223.16.242.216 (talk) 06:03, 30 April 2024 (UTC)[reply]

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