similar to: Calculating sum of squares deviation between 2 similar matrices

On Mon, Mar 14, 2016 at 02:09:13AM +0530, Richhiey Thomas wrote: > The way the paper has been written I guess is the main source of your > confusion. Let me provide a paper that explains this same concept in a way > that is easier to understand. I was confused by eq (3) that you mentioned > too. Here it is : > http://www.sau.ac.in/~vivek/softcomp/clustering%20PSO+K-means.pdf Ah,

Dear R users, I have noted a difference in the merge distances given by hclust using centroid method. For the following data: x<-c(1009.9,1012.5,1011.1,1011.8,1009.3,1010.6) and using Euclidean distance, hclust using centroid method gives the following results: > x.dist<-dist(x) > x.aah<-hclust(x.dist,method="centroid") > x.aah$merge [,1] [,2] [1,] -3 -6

Hello, I tried to make a betadisper plot; however, it is quite messy at the moment with lines and symbols. I made two plots, one focusing on sites and the other on treatments. This is the code that I used: plot(betadisper(vegdist(y.nth,method="euclidean"),site)) plot(betadisper(vegdist(y.nth,method="euclidean"),treatment)) I have a few questions pertaining to how I could

Hi list, I have a Samba-3.0.25a PDC running on FreeBSD 6.2-STABLE using OpenLDAP 2.3.34 (nss_ldap-1.255) as backend. Everything work's great, the only problem that I fighting is with an M$ ISA Server 2000, that do ntlm authentications on my domain. At some times (each 4 hours) the ISA Server lost the connection with the domain and does not authenticate users until the connection be

Hello, I need to calculate the centroids of some spatial polygons that I have placed into a Polygons-class object. Is the labeling point in the Polygons-class the centroid of the polygon? Thank you for your help.

Hello, I have carried out an lda analysis using the lda function of MASS package. I have plotted the LD1xLD2 to represent the data. Now I would like to get the centroids for each group of data and plot it on the LD1xLD2 graph. How can I get the centroid value from the lda object? Best, Dani -- Daniel Valverde Saub? Grup de Biologia Molecular de Llevats Facultat de Veterin?ria de la

Hi list! I'm computing multivar. distances from a set of centroids to a (large) set of individuals. I'm now just using rbind to create a matrix (x) with the centroid and the individuals, then run as.matrix(dist(x)) and finally select the appropriate columns, as I'm not interested on the distances among individuals. Therefore, this procedure implies a waste of computing time. Is there

On Sun, Mar 6, 2016 at 7:17 AM, James Aylett <james-xapian at tartarus.org> wrote: > On Sat, Mar 05, 2016 at 10:58:43PM +0530, Richhiey Thomas wrote: > > K-Means or something related certainly seems like a viable approach, > so what you'll need to do is to come up with a proposal of how you'd > implement this in Xapian (either with reference to the previous work, >

10/10/2007 After consulting with some local Linux consultants, we have concluded that Corel Draw does not print under WINE because it detects the printer as Postscript, converts the file to Postscript, and sends the file to CUPS, which then converts the file to Postscript, again, and sends it to the printer which prints garbage. The consultant found a way to make a couple changes to WINE

Helo there. I am using kmeans of base package to cluster my customers. As the results of kmeans is dependent on the initial centroid, may I know: 1) how can we specify the centroid in the R function? (I don't want random starting pt) 2) how to determine the optimal (if not, a good) centroid to start with? (I am not after the fixed seed solution as it only ensure that the

Hey Parth, Thanks for the reply. I am considering implementing a cosine distance metric too, along with euclidian distance because of the dimensionality issue that comes in with K-Means and euclidian distance metric. That does help when we deal with sparse vectors for documents. The particular problem I'm having is representing centroids in an efficient way. For example, when we find the mean

Hello, I've been working on the KMeans clustering algorithm recently and since the past week, I have been stuck on a problem which I'm not able to find a solution to. Since we are representing documents as Tf-idf vectors, they are really sparse vectors (a usual corpus can have around 5000 terms). So it gets really difficult to represent these sparse vectors in a way that would be

Hi all, I've got some functioning code that I've literally taken hours to write. My 'R' coding is getting better...it used to take days :) I know I've done a poor job of optimizing the code. In addition, I'm missing an important step and don't know where to put it. So, three questions: 1) I'd like the resulting output to be sorted on distance (ascending) and

Art (and group), I'm doing this as a form of missing value analysis. Approximately 30% of the cases are missing data for one variable. To impute values for those cases, I'd like to match those cases that are missing the variable to all other cases and then take an average of those to infill. I realize there are many methods for imputing data. I'm not well versed on any in

Hey, all, I'm trying to construct a centroid plot using canonical values from a MANOVA. I know that from the summary.manova object you can get Eigenvalues, and the H and E matrices (from SS$Treatment and SS$Residuals), but I am at a loss to get the loadings for the canonical values, nor values for the centroid centers and radii. Is there a package that does this that I am just missing,

On 18 Aug 2016, at 23:59, Richhiey Thomas <richhiey.thomas at gmail.com> wrote: > I've currently added a few classes which don't really belong to the public API (currently) into private headers and used PIMPL with the Cluster class. I'm having difficulty reading your changes, because you aren't keeping to one complete change per commit. So for instance you've added a

Dear All, I have several socio-economic and geographic variables for the 27 EU countries. I would to use these data to derive a correlation matrix between groups of countries (for a different application). I thought of using kmeans to cluster the groups, and then calibrate between group correlations using distances between the centroids, and within group correlations using distances in a cluster

Version 4.39 of the caret package was sent to CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 75 different models for classification and regression. See the package

Version 4.39 of the caret package was sent to CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 75 different models for classification and regression. See the package

Hello, I've recently finished with an implementation of KMeans with two initialization techniques, random initialization and KMeans++. I would like to share my findings after evaluating the same. I have tested this implementation of KMeans with a BBC news article dataset. I am currently working on evaluating the same with FIRE datasets. Currently, clustering more than 500 documents