Hi, I think "truncated" normality is what I meant in the last email. The experiments (, which might not be representative) show k-mean and EM gave me comparable results, while EM is a little bit better. (I used Weka for this purpose). I will try them on the real data and also try pam() and if I have clear conclusion, I will post my result as a future suggestion.
Thanks for all you guys' help! Ed On Fri, 28 Jan 2005 11:51:44 +0100 (MET), Christian Hennig <[EMAIL PROTECTED]> wrote: > Hi, > > EMclust in package mclust fits normal mixtures. > Note that if you split your data values into intervals, the resulting > distributions conditional on the intervals are not normals, but truncated > normals! > This is important if you try to check within group normality, unless you > have strongly separated clusters (which does not seem to be the case). > > Christian > > > On Fri, 28 Jan 2005, WeiWei Shi wrote: > > > Actually the problem I am trying to solve is to discretize a > > continuous variable (which is my response variable (dependent > > variable) in my project so that I can make a regression problem into a > > classification one. (There are many reasons for doing this.) > > > > Since there is no class label for this variable (because this variable > > is my class variable :), the unsupervised approach can be applied > > here. However, checking the related papers shows there is little > > research (in my knowledge, and I haven't checked the MCC yet) in this > > field. Using qqnorm to check the normality and histogram indicates > > there might be two normal distributions. > > > > My approach is splitting the values for this variable into 2 or 3 > > intervals and check each interval's normality again. If some approach > > like clustering or the one Andy suggests works well, then I should get > > much better normality. I will try that tomorrow. > > > > I am not sure if my idea works or not here, please be advised ! > > > > Thanks, > > > > Ed > > > > > > On Thu, 27 Jan 2005 18:58:28 -0500, Liaw, Andy <[EMAIL PROTECTED]> wrote: > > > It depends a lot on what you know or don't know about the data, and what > > > problem you're trying to solve. > > > > > > If you know for sure it's a mixture of gaussians, likelihood based > > > approaches might be better. MASS (the book) has an example of fitting > > > univariate mixture of gaussians using various optimizers. The code is > > > even > > > in $R_HOME/library/MASS/scripts/ch16.R. > > > > > > Andy > > > > > > > From: WeiWei Shi > > > > > > > > Hi, > > > > thanks for reply. In fact, I tried both of them and I also tried the > > > > other method and I found all of them gave me different boundaries (to > > > > my real datasets). I am thinking about k-median but hoping to get more > > > > suggestions from all of you in this forum. > > > > > > > > Cheers, > > > > > > > > Ed > > > > > > > > > > > > On Thu, 27 Jan 2005 15:37:16 -0600, [EMAIL PROTECTED] > > > > <[EMAIL PROTECTED]> wrote: > > > > > The cluster analysis should be able to handle that. I think if you > > > > > know how many clusters you have, "kmeans" is ok, or the EM algorithm > > > > > can also do that. > > > > > On Thu, Jan 27, 2005 at 03:44:42PM -0500, WeiWei Shi wrote: > > > > > > Hi, > > > > > > I just get a question (sorry if it is a dumb one) and I "phase" my > > > > > > question in the following R codes: > > > > > > > > > > > > group1<-rnorm(n=50, mean=0, sd=1) > > > > > > group2<-rnorm(n=20, mean=1, sd=1.5) > > > > > > group3<-c(group1,group2) > > > > > > > > > > > > > > > > > > Now, if I am given a dataset from group3, what method > > > > (discriminant > > > > > > analysis, clustering, maybe) is the best to cluster them > > > > by using R. > > > > > > The known info includes: 2 clusters, normal distribution (but the > > > > > > parameters are unknown). > > > > > > > > > > > > Thanks, > > > > > > > > > > > > Ed > > > > > > > > > > > > ______________________________________________ > > > > > > [email protected] mailing list > > > > > > https://stat.ethz.ch/mailman/listinfo/r-help > > > > > > PLEASE do read the posting guide! > > > > http://www.R-project.org/posting-guide.html > > > > > > > > > > > > > > > > > ______________________________________________ > > > > [email protected] mailing list > > > > https://stat.ethz.ch/mailman/listinfo/r-help > > > > PLEASE do read the posting guide! > > > > http://www.R-project.org/posting-guide.html > > > > > > > > > > > > > > > > > ------------------------------------------------------------------------------ > > > Notice: This e-mail message, together with any attachment...{{dropped}} > > > > ______________________________________________ > > [email protected] mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide! > > http://www.R-project.org/posting-guide.html > > > > *********************************************************************** > Christian Hennig > Fachbereich Mathematik-SPST/ZMS, Universitaet Hamburg > [EMAIL PROTECTED], http://www.math.uni-hamburg.de/home/hennig/ > ####################################################################### > ich empfehle www.boag-online.de > > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
