Hi,
Thanks for reply but I already read the help page I am new in R and did not
understand the output description of kmeans -function. That is why I wanted
to ask some experts in the group.
My point is that I do not understand which data are combined in the specific
cluster?
I tried the following
ehalf Of Dzu
Sent: Monday, June 24, 2013 4:25 AM
To: r-help@r-project.org
Subject: [R] K-means results understanding!!!
Dear members.
I am having problems to understand the kmeans- results in R. I am
applying
kmeans-algorithms to my big data file, and it is producing the
results of
the clusters.
Q1)
Dear members.
I am having problems to understand the kmeans- results in R. I am applying
kmeans-algorithms to my big data file, and it is producing the results of
the clusters.
Q1) Does anybody knows how to find out in which cluster (I have fixed
numberofclusters = 5 ) which data have been used?
;M University
College Station, TX 77843-4352
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of olemissrebs1123
> Sent: Tuesday, August 28, 2012 3:16 PM
> To: r-help@r-project.org
> Subject: [R] K-Means clustering Algorith
I was wondering if there was an R equivalent to the two phased approach that
MATLAB uses in performing the Kmeans algorithm. If not is there away that I
can determine if the kmeans in R and the kmeans in MATLAB are essentially
giving me the same clustering information within a small amount of erro
Ferebee Tunno mathstat.astate.edu> writes:
> Hi everyone -
>
> I know that R is capable of clustering using the k-means algorithm, but can
> R do k-means++ clustering as well?
k-means++ is a routine to suggest center points before the classical k-means
is called. The following lines of code wi
Hi everyone -
I know that R is capable of clustering using the k-means algorithm, but can
R do k-means++ clustering as well?
Thanks,
--
Dr. Ferebee Tunno
Assistant Professor
Department of Mathematics and Statistics
Arkansas State University
P.O. Box 70
State University, AR. 72467
ftu...@astate.
Try the pch() argument to plot(), or perhaps using text(), depending
on what exactly you're trying to achieve.
Sarah
On Tue, Dec 13, 2011 at 4:03 PM, Meesters, Christian wrote:
> Hi,
>
> For my data, I followed the example of
> http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering
Hi,
For my data, I followed the example of
http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/K-Means#Execution
and got some very nice results. Despite the fact, that I want to achieve a bit
more by clustering my data (stratification beyond case-control), the actual
data-frame
See
Ligges, U. (2006): R Help Desk: Accessing the Sources. R News 6 (4), 43-45.
Best,
Uwe Ligges
On 07.04.2011 16:05, Jean-Eudes Dazard wrote:
Dear R user,
How can I get the C or C++ source code of the "R_kmeans_MacQueen" or
"R_kmeans_Lloyd" subroutines implemented in the R "kmean
Dear R user,
How can I get the C or C++ source code of the "R_kmeans_MacQueen" or
"R_kmeans_Lloyd" subroutines implemented in the R "kmeans" function of the R
stats-package {stats}? Would these be available as a C header file (*.h)
somewhere from the R installation???
Any help to get
On Fri, Jul 2, 2010 at 4:37 AM, Ralph Modjesch
wrote:
> Hi,
>
> I like to present the results from the clustering method k-means in
> terms of variances: within and between Cluster. The k-means object
> gives only the within cluster sum of squares by cluster, so the between
> variance part is miss
Dear Ralph,
between and within clusters sum of squares (if you want variances, you
need to divide them by the appropriate constant!) add up to the
overall sum of squares, so you can get the beween clusters ss by
computing the overall ss (one possibility to get this is to run kmeans
with k=1)
Hi,
I like to present the results from the clustering method k-means in
terms of variances: within and between Cluster. The k-means object
gives only the within cluster sum of squares by cluster, so the between
variance part is missing,for calculation the following table, which I
try to get.
Numb
That kmeans returns an error if there is an empty cluster is a bit of a
nuisance.
It should not be too difficult to get rid off the kmeans function for what
you call "reclustering". You could write your own function that assigns
every point of the new data to the closest initial center. That s
K-means recluster data with given cluster centers
Dear R user,
I have several large data sets. Over time additional new data sets will be
created.
I want to cluster all the data in a similar/ identical way with the k-means
algorithm.
With the first data set I will find my cluster centers and s
I am running a k-means clustering code in R : mydata_kmeans5 <-
kmeans(mydata, centers=5).. But the problem is that the data is having some
"NA" in it. So R is showing me a message :Error in switch(nmeth, { :
NA/NaN/Inf in foreign function call (arg 1)
In addition: Warning messages:
1: In switch(n
Unfortunately, your data is *not* numeric. That is what the first
error message, " 'x' must be numeric", is telling you, and you should
believe it. It might look numeric, but it isn't, which is why Ingmar
mentioned you might have factors instead of numbers.
Your challenge is to discover why. T
On 9 May 2008, at 09:12, Jordan van Rijn wrote:
> Hello,
>
> I am hoping you can help me with a question concerning kmeans
> clustering
> in R. I am working with the following data-set (abbreviated):
>
>
> BMW Ford Infiniti Jeep Lexus Chrysler Mercedes Saab Porsche
> Volvo
> [
Hello,
I am hoping you can help me with a question concerning kmeans clustering
in R. I am working with the following data-set (abbreviated):
BMW Ford Infiniti Jeep Lexus Chrysler Mercedes Saab Porsche
Volvo
[1,] 6828 4544 7 7
k-means uses Euclidean distance, so scaling of the variables does matter.
Whether you want to standardize depends on the example (as it does in most
multivariate analysis problems, e.g. PCA has the same issues).
On Tue, 22 Apr 2008, Johan Jackson wrote:
> Hi all,
>
> Simple question re k-means.
Hi all,
Simple question re k-means. If I have a data set with columns that are on
different scales (say col 1 has var=100 and col2 var=2), will this make a
difference to the k-means algorithm? It seems as though it does. If so,
should we first standardize the columns of the dataset so that each co
essage-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of Aylward, Jesse
Sent: Wednesday, 6 February 2008 7:17 AM
To: r-help@r-project.org
Subject: [R] K Means Clustering Weighted by Frequency
*Apologies if this is not the right way to ask a question, I'm a first
timer posting here.
*Apologies if this is not the right way to ask a question, I'm a first
timer posting here.
Does anyone have a solution to this? I'm having trouble figuring out
how to use weighting with K Means Clustering.
So say if my dataset is:
Column 1 = x coords
Column 2 = y coords
Column 3 = frequency
Hi David,
That area/topic you flagged is unusual to say the least in the grand scheme of
what I have read in the coverage of k-means.
I have been using k-means for many years, and have never come across this
before (maybe out of ignorance and not keeping abreast of all the issues
associated
Googling for sphericity gives wikipedia as a first link which says:
Sphericity is a measure of how spherical (round) an object is.
The second hit gives the connection with statistics, in particular
ANOVA,
http://www.linguistics.ucla.edu/faciliti/facilities/statistics/spher.htm
hth, Ingmar
On S
Dear list, first apologies for this is not strictly an R question but
a theoretical one.
I have read that use of k-means clustering assumes sphericity of data
distribution. Can anyone explain me what this means? My statistical
background is too poor. Is it another kind of distribution, like
g
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