search for: medoids

Hello R-community: Much of the time I want to use loops to look at graphs, etc. For example, I have 25 plots, for which the names are m.1$medoids, m.2$medoids, ..., m.25$medoids. I want to index the object number (1:25) as below (just to show concept). for (i in 1:25){ plot(m.i$medoids) } I've tried the following, with negative results for ... plot(paste("m.",i,"$medoids",sep="")) for .. plot(get(paste(&...

Greetings, I have had good success using the clara() function to perform a simple cluster analysis on a large dataset (1 million+ records with 9 variables). Since the clara function is a wrapper to pam(), which will accept known medoid data - I am wondering if this too is possible with clara() ... The documentation does not suggest that this is possible. Essentially I am trying to

Hello, When using PAM (partitioning around medoids), I would like to skip the build-step and give the fonction my own medoids. Do you know if it is possible, and how ? Thank you very much. Isabel

I am using the PAM algorithm in the CLUSTER library. When I allow PAM to seed the medoids using the default __build__ algorithm things work well: > pam(stats.table, metric="euclidean", stand=TRUE, k=5) But I have some clusters from a Hierarchical analysis that I would like to use as seeds for the PAM algorithm. I can't figure what the mediod argument wants. When I put...

...this is not supported by the function "kmeans", I turned to the "pam" function in the "fpc" package. Yet, when I tried to have the algorithm run with different starting points, I found that pam ignores and keep on starting the algorithm from the same starting-points (medoids). For my questions: 1) is there a bug in the code or in the way I am using it ? 2) is there a way to either fix the code or to another function in some package that can run kmeans with manhattan distance (manhattan distances are the sum of absolute differences) ? here is a sample code: require(fp...

...---------------------------- res <- array(0,c(nc.max - nc.min +1,2)) res[,1] <- nc.min:nc.max clusters <- NULL for (nc in nc.min:nc.max) { cl <- pam(dist.mat,nc,diss=TRUE) res[nc-nc.min+1,2] <- G1 <- index.G1(as.matrix(alpha.vec),cl$cluster,d=dist.mat,centrotypes="medoids") clusters <- rbind(clusters, cl$cluster) } ############################################################################### I get the following error whenever I use index.G1: > nc <- 2 > cl <- pam(dist.mat,nc,diss=TRUE) > cl Medoids: ID [1,] 5 5 [2,] 9 9 Clus...

...stion (I hope): How can I compute the "sum of the dissimilarities" that appears in the pam command (from the cluster package) ? Is it the "manhattan" distance (such as the one implemented by "dist") ? I am asking since I am running clustering on a dataset. I found 7 medoids with the pam command, and from it I have the medoid to which each observation belongs to. But when I check it, I find only (about) 90% of observations has the minimum manhattan distance to the medoids that pam predicted. If this is the manhattan distance that is used, I will create some toy data t...

Dear all, I have a problem when using some classification functions (Kmeans, PAM, FANNY...) with a distance matrix, and i would to understand how it proceeds for the positioning of centroids after one execution step. In fact, in the classical formulation of the algorithm, after each step, to re-position the center, it calculates the distance between any elements of the old cluster and its

Hello everyone, I need to do k-medoids clustering for data which consists of 50,000 observations. I have computed distances between the observations separately and tried to use those with pam(). I got the "cannot allocate vector of length" error and I realize this job is too memory intensive. I am at a bit of a loss on what...

Hola. Yo en vez de utilizar análisis cluster que impliquen distancias, probaría con un kmedias o con un pam (partition around medoids) pero utilizando muestras, la función clara de la librería cluster puede ayudarte. Pego el details de la ayuda de 'clara' Details clara is fully described in chapter 3 of Kaufman and Rousseeuw (1990). Compared to other partitioning methods such as pam, it can deal with much larger dat...

HI R users With clara function I get a data frame (maybe this is not the exact word, I'm new to R) with the following variables: > names(myclara) [1] "sample" "medoids" "i.med" "clustering" "objective" [6] "clusinfo" "diss" "call" "silinfo" "data" I want to export "clustering" and "data" to a new text file so I try > write.table(...

...; and (if I remember correctly) 'cluster' seem to be really bad through very thorough experimentation of data generation with known clusters. I am left then with either theoretical abstractions such as pruning hclust trees with minimal spanning trees or perhaps hand-rolling a hierarchical k-medoids which works extremely efficiently and without cluster number assumptions. Anybody have any suggestions as to possible libraries which I have missed or suggestions in general? Note: this is not a question for 'Bigkmeans' unless there exists a 'findbigkmeansnumberofclusters' function...

.../k-means-clustering-on-big-data/ Javier Marcuzzi De: jose luis cañadas Enviado el: ?miércoles?, ?29? de ?abril? de ?2015 ?02?:?10? ?p.m. Para: R-help-es en r-project.org Hola. Yo en vez de utilizar análisis cluster que impliquen distancias, probaría con un kmedias o con un pam (partition around medoids) pero utilizando muestras, la función clara de la librería cluster puede ayudarte. Pego el details de la ayuda de 'clara' Details clara is fully described in chapter 3 of Kaufman and Rousseeuw (1990). Compared to other partitioning methods such as pam, it can deal with much larger dataset...

Hi, I wanted the DICE coefficient (similarity measure for binary variables) to be calculated in R and found that the "igraph" package has the option of "similarity.dice" to do this. But, for this command, the input object should be an igraph object. But, I have a dataframe of columns containing 1's and 0's. Can I convert this dataframe into an igraph object, so that

Hello list, I am new to R, so if the question is rather silly, please ignore it. I was wondering wether it would be possible to use the models generated by pam, clara and the like as predictors? Scanning through the available documentation shed no light (for me) upon the subject. Regards, Renald

Hi there, Sorry for the double email. Does R have the packages for the following clustering methods? And if it does, what the commands for them? 1. SOM (Self-organization map) 2. Graph partitioning: 3. Neural network 4. Probability Binning Thank you very much! Linda [[alternative HTML version deleted]]

...lt;-fanny(x,2) But it seems like none of them have exactly the same components as "kmeans" (Please see "P.S."). Could you please help me clearify which methods included in the argument "base.method"? Thank you! P.S.: For example "clara": Medoids: Objective function: Clustering vector: Cluster sizes: Best sample: Available components: [1] "sample" "medoids" "i.med" "clustering" "objective" "clusinfo" "diss" [8] "ca...

...1 2 2 . . . [8696] 2 1 1 2 1 1 1 1 2 2 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 2 2 2 2 2 2 2 1 2 2 2 [8733] 2 2 1 1 2 1 2 2 1 2 2 1 1 2 1 2 2 1 2 2 2 2 1 2 2 2 1 2 1 2 2 2 1 2 1 1   to a single vertical column? Thanks.   I used the following code to arrive at the above output: pam(dm,2,diss=TRUE, medoids=NULL, cluster.only=FALSE,do.swap=TRUE, keep.data=FALSE, trace.lev=0)   Penny [[alternative HTML version deleted]]

...De: jose luis cañadas Enviado el: ?miércoles?, ?29? de ?abril? de ?2015 ?02?:?10? ?p.m. Para: R-help-es en r-project.org<mailto:R-help-es en r-project.org> Hola. Yo en vez de utilizar análisis cluster que impliquen distancias, probaría con un kmedias o con un pam (partition around medoids) pero utilizando muestras, la función clara de la librería cluster puede ayudarte. Pego el details de la ayuda de 'clara' Details clara is fully described in chapter 3 of Kaufman and Rousseeuw (1990). Compared to other partitioning methods such as pam, it can deal with much larger da...

...values (e.g. Full-time Employment, Student,...) Only cases with at least one change in labour market status is included in the data set. To analyse sub sets of the data, I have used daisy in the cluster-package to create a distance matrix and then used pam (or pamk in the fpc-package), to get a k-medoids cluster-solution. Now I want to analyse the whole set. clara is said to cope with large data sets, but the first step in the cluster analysis, the creation of the distance matrix must be done by another function since clara only works with numeric data. Is there an alternative to the daisy ->...