similar to: Dbscan Clustering Feature Question

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

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

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

Hello all. I wish to run k-means with "manhattan" distance. Since 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

hi there, whenever i try the clusterwise regression from the fpc package, there occurs the following problem: the first cluster is always designed in a way, that when i run a normal linear regression on the independent variables to describe the dependent variable (only on those respondents from the first cluster) - then the regression uses only one independent variable that describes the whole

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.

Dear WizaRds, I have been trying to compute the adjusted Rand index as by Hubert/ Arabie, and could not correctly approach how to define a partition object as in my last request yesterday. With package fpc I try to work around the problem, using my original data: mat <- matrix( c(6,7,8,2,3,4,12,14,14, 14,15,13,3,1,2,3,4,2, 15,3,10,5,11,7,13,6,1, 15,4,10,6,12,8,12,7,1), ncol=9, byrow=T )

Greetings R-users, I have been using the fpc package in R to cluster my data. Speficically I am using kmeansruns clustering. I would like to know how I use R to partition data into clusters. What I am doing is as follows. # Use csv file as input ##################### wholeset = read.csv("Spellman800genesImputed.csv") # exclude first col (gene names) ##########################

Hello R users It's some time I am playing with a dataset to do some cluster analysis. The data set consists of 14 columns being geographical coordinates and monthly temperatures in annual files latitutde - longitude - temperature 1 -..... - temperature 12 I have some missing values in some cases, maybe there are 8 monthly valid values at some points with four non valid. I don't want to

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

Main source: http://radio.stalig.com:443/radio-bro-gwened-ori.ogg reencoded OGG: http://radio.stalig.com:443/radio-bro-gwened-16.ogg http://radio.stalig.com:443/radio-bro-gwened.ogg http://radio.stalig.com:443/radio-bro-gwened-64.ogg reencoded to mp3: http://radio.stalig.com:443/radio-bro-gwened.mp3 (very bad, but mp3 is here just to have some mp3 in case of the listener is under Mac OS9 or

Hello, ogg123 | ices2 are doing transcoding but ogg123 is staying frozen under certain circumstances here is the stack #0 0x401f25d4 in __pthread_sigsuspend () from /lib/libpthread.so.0 #1 0xbffff94c in ?? () #2 0x401f2398 in __pthread_wait_for_restart_signal () from /lib/libpthread.so.0 #3 0x401eef0b in pthread_cond_wait@GLIBC_2.0 () from /lib/libpthread.so.0 #4 0x0804b0d3 in

Dear WizaRds, I am struggling to compute correctly a cluster sampling design. I want to do one stage clustering with different parametric changes: Let M be the total number of clusters in the population, and m the number sampled. Let N be the total of elements in the population and n the number sampled. y are the values sampled. This is my example data: clus1 <-

Hello, I have a problem with icecast icecast-2.0-kh8: - the source disconnect - icecast didn't notice it - the source cannot reconnect until - the source cannot reconnect (it tries every 8 seconds) - after two hours, the source is able to reconnect Here is the interesting parts of the log files: Thanks for your advices and help. Chris (icecast machine has +3sec in its time, that's

Hello, I had a problem with ices kh48 and icecastkh13. I usually restart them by cron at 00:00. ----------1---------- This night, icecast told ices "Too many sources" for radio-bro-gwened.ogg... All right, I've just seen that my config file is wrong for icecast (I have 3 sources): <sources>2</sources> But, with this configuration, I can have 3 sources:

Hi, I'm trying to implement the DBSCAN algorithm to get O(N*LogN) complexity and I'd need a spatial tree of some sort (kd,r,bd..), or a function that computes radius-based search on spatial data, i.e. given a radius eps finds ALL the points which fall in the corresponding hypersphere centered on the current examined point. Is there a package with this features? So far I found RANN

Hello, I have some troubles with the latest icecast kh. I'm not sure it an icecast problem, but I have no idea... Does someone have an idea about that problem? The thing is that icecast is running on a uml linux. But this worked some weeks ago, then I come back to it, and I can't get it work again... Thanks, Chris <p>stalwook:~$ wget -Sd http://rbg.XXXXnet/radio-bro-gwened.ogg

Hi, this is to announce a new version (1.1-2) of my package fpc. Apart from the stuff already present in the older version (methods for fixed point clustering and clusterwise regression, somewhat bug-cleaned and with faster examples) there is now a function cluster.stats, which computes some distance-based statistics often used for cluster validation, description and decision about the number of

Hi, this is to announce a new version (1.1-2) of my package fpc. Apart from the stuff already present in the older version (methods for fixed point clustering and clusterwise regression, somewhat bug-cleaned and with faster examples) there is now a function cluster.stats, which computes some distance-based statistics often used for cluster validation, description and decision about the number of