Talk:Determining the number of clusters in a data set
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Additional updates coming
A colleague will be adding details to the "Elbow method" and "Information criteria" subsections shortly. -JohnMeier (talk) 15:10, 9 April 2009 (UTC)
Not that common problem
There are lots of alternative algorithms that do not require the specification of k beforehand. This is mostly a problem of k-means, k-medoids and the EM-algorithm. Pretty much none of the more recent algorithms has this parameter. --Chire2 (talk) 14:13, 7 May 2010 (UTC)
- Any examples for such algorithms? thanks. Talgalili (talk) 12:36, 20 June 2010 (UTC)
A well known, early example is the AutoClass algorithm, by Cheeseman et al. 1988, which applied a search-based method built around Expectation Maximization to find the Maximum A-Posteriori distribution as a function of the number of classes. More modern approaches to this problem would equivalently apply the Bayes Information Criterion to selecting k. Johnmark54 (talk) 15:26, 5 October 2011 (UTC)
Spectral Methods
Spectral methods automatically give k for many datasets. — Preceding unsigned comment added by 192.249.47.174 (talk) 15:38, 21 June 2012 (UTC)
REference to such methods please? — Preceding unsigned comment added by 152.16.225.159 (talk) 19:24, 31 October 2012 (UTC)
Information and text
Information theoretic section is disproportionately long, it should be edited down to be commensurate with the others.
Also, I moved the heuristic about textual clustering down, as it is specialized and not of very general interest (compared to, say, the elbow method). — Preceding unsigned comment added by Bluedevil.knight (talk • contribs) 14:46, 1 November 2012 (UTC)
Note equivalent to the F test
Original says: " Percentage of variance explained is the ratio of the between-group variance to the total variance, also known as an F-test. " This seems wrong. For one, the F-test is not this ratio. The F-statistic is between-group variance over within-group variance, which does not give percent of variance explained. Percentage of total variance explained is between-group variance over total variance, or the variance of your data set. IF nobody refutes this, I will modify the original. Bluedevil.knight (talk) 19:13, 1 November 2012 (UTC)