similar to: Traversing KD-tree (or equivalent) for radius-based search

Hello everyone, When looking for information about clustering of spatial data in R I was directed towards DBSCAN. I've read some docs about it and theb new questions have arisen. DBSCAN requires some parameters, one of them is "distance". As my data are three dimensional, longitude, latitude and temperature, which "distance" should I use? which dimension is related to

Hi people, Does anybody know some Density-Based Method for clustering implemented in R? Thanks, Fernando Prass _______________________________________________________

Hello list. My apologies if this topic has been discussed before on the list but I was unable to find it. I'm working on a way to cluster PCAP files according to the events recorded within them. I've decided to use Bro-IDS for feature extraction. I am looking at dbscan within the FPC library to accomplish my goal. Is it possible to feed a data frame to dbscan with more than two columns

Hi All , For an academic project I am trying to do the following Step 1 ) Draw and cluster a N ( lets say 3 ) column dataset by dbscan algorithm using R-project’s fpc package ( let say they are "training clusters" ) , Using dbscan as number of clusters are not know before hand Step 2 ) Once that is done i want to spread some new data points in the

Dear R users, I?m please to announce the available on CRAN of new package largeVis.(*) largeVis offers three major features: - A fast implementation of the LargeVis algorithm. LargeVis is for visualizing high-dimensional datasets, similar to (and of similar quality to) t-SNE. But, LargeVis runs in O(n) time, which makes it feasible to use on datasets with millions of rows and thousands of

Dear R users, I?m please to announce the available on CRAN of new package largeVis.(*) largeVis offers three major features: - A fast implementation of the LargeVis algorithm. LargeVis is for visualizing high-dimensional datasets, similar to (and of similar quality to) t-SNE. But, LargeVis runs in O(n) time, which makes it feasible to use on datasets with millions of rows and thousands of

I saw that R language has a cluster package which has in built PAM, CLARA and Kmeans (and many more) Clustering Algorithms. But, I couldnot find DBSCAN, ROCK, BIRCH algorithms (which I feel are standard ones). Aren't these implemented as well? Bhanu Kalyan K B.Tech Final Year, CSE Tel: +91-9885238228 Alternate E-Mail: reach4kalyan@gmail.com --------------------------------- We

There are numerous tools like scons, make, ruffus, ant, rake, etc. that can be used to build complex pipelines based on task dependencies. These tools are written in a variety of languages, but I have not seen such a thing for R. Is anyone aware of a package available? The goal is to be able to develop robust bioinformatic pipelines driven by scripts written in R. Thanks, Sean

Is it possible to implement my own distance and mean for k-means clustering for any clustering package in R? Just looking for simple way, without creating a new package. karsar

All, How can we find a distance matrix for categorical data ie. given a csv below var1 var2 var3 var4 element1-1 yes x a k element1-2 no y b l element1-3 maybe y c m how can i compute the distance matrix between all the elements Actually i need it to create clusters on top

I use kmeans to classify spectral events in high and low 1/3 octave bands: #Do cluster analysis CyclA<-data.frame(LlowA,LhghA) CntrA<-matrix(c(0.9,0.8,0.8,0.75,0.65,0.65), nrow = 3, ncol=2, byrow=TRUE) ClstA<-kmeans(CyclA,centers=CntrA,nstart=50,algorithm="MacQueen") This works well when the actual data shows 1,2 or 3 groups that are not "too close" in a cross plot.

I have corrected a typo in my previous posting. In what follows the line with the inequality is correct Hi , I have two sorted vectors X and Xi, where the range of Xi lies within the range of X. For an element in Xi, I want to find the neigbouring data in X, e.g. find an index ix so that for element number k, then X[ix[k]] < Xi[k] < X[ix[k] +1] # also OK with "<=" on either

Dear R-help-list, The following is R function I wrote for computing multi-dimensional kernel density. I am seeking R experts who can make the code to run faster, 50 times faster ideally. Specifically, for function kernel.estimate = function(points, bw), the argument points is a d by n matrix as the n points in the d-dimensional space, bw is the bandwidth. The function will compute the kernel

I am looking for an efficient way to find near neighbors... More specifically... I have two sets of points: A & B and I want to find points in set B which are closer to set A than some cutoff (or n-closest) I will appreciate very much any pointers... Ryszard -------------------------------------------------------------------------- Confidentiality Notice: This message is private and may

Up, Any helps in this would be really appreciated. I stuck with this. Regards --- On Mon, 6/13/11, saleh [via R] <ml-node+3593743-1399725529-244676@n4.nabble.com> wrote: From: saleh [via R] <ml-node+3593743-1399725529-244676@n4.nabble.com> Subject: Calling R from Java To: "saleh" <s.alhammed@yahoo.com> Date: Monday, June 13, 2011, 12:35 PM Dear Sir/Madam, Sorry

Hi, all. I'm trying to cluster 12,500 objects using hclust from package mva. The distance matrix takes up nearly 600 MB. The distance matrix also needs to be copied when being passed to the fortran routine that actually does the clustering (it's modified during the clustering), so that's 1200 MB. I'm actually on a machine with 2.5 GB of memory (and nothing else running), so I

Dear all, Objective: I am trying to learn about neural networks. I want to see if i can train an artificial neural network model to discriminate between spam and nonspam emails. Problem: I created my own model (example 1 below) and got an error of about 7.7%. I created the same model using the Rattle package (example 2 below, based on rattles log script) and got a much better error of about

Hi all, I'm running a monte carlo test of a neural network tool I've developed, and it looks like it's going to take a very long time if I run it in R so I'm interested in translating my code (included below) into something faster like Fortran (which I'll have to learn from scratch). However, as you'll see my code loads the nnet library and uses it quite a bit, and I

Hi, Dear all, I am just starting using R in my work and got some trouble to figure out some of the errors. Can anybody help me? The following is the script: read.csv('pupil.txt',header=TRUE,sep='\t')->pupil samp<-c(1:50, 112:162, 171:220, 228:278) pupil.nn2 <- nnet(Type ~ ., data = pupil, subset = samp, size = 2, rang = 0.1, decay = 5e-4, maxit = 200)

Dear R, I have a covariates matrix with 10 observations, e.g. > X <- matrix(rnorm(50), 10, 5) > X [,1] [,2] [,3] [,4] [,5] [1,] 0.24857135 0.30880745 -1.44118657 1.10229027 1.0526010 [2,] 1.24316806 0.36275370 -0.40096866 -0.24387888 -1.5324384 [3,] -0.33504014 0.42996246 0.03902479 -0.84778875 -2.4754644 [4,] 0.06710229 1.01950917