similar to: cluster benchmark datasets

Tf-idf is most used used weighting scheme is easy to understand and has been used in other frameworks like lucene and many other places. okapi bm25(implemented in xapian) is theoretically better/improved measure than tf-idf and i am looking into various other weighting scheme which are there in xapian or can be implemented like TF-ICF(term frequecy inverse corpus frequency),TF-RF(term

From: Peter Arremann <loony at loonybin.org> > It is a valid benchmark though :-) compile speed is a actually a > good measure for any integer app that is small enough to run in > large cache... Image processing, oil companies for their simulations, > cad... they all act very similar to compile benchmark - if a compile > is twice as fast, a software image rendering is usually

Hello R list members, I'm a bio informatics student from the Leiden university (netherlands). We were asked to make a program with different clustering methods. The problem we are experiencing is the following. we have a matrix with data like the following research1 research2 research3 enz sample1 0.5 0.2 0.4 sample2 0.4

Hi all, I have a dataset with numeric and factor columns of data which I developed a Gower Dissimilarity Matrix for (Daisy) and used Agglomerative Nesting (Agnes) to develop 20 clusters. I would like to use the 20 clusters to determine cluster membership for a new dataset (using predict) but cannot find a way to do this (no way to "predict" in the cluster package). I know I can use

Dear R experts, I have a labeled data set. Each data is assigned a binary label 0 or 1. Assume that I use some clustering algorithm to group the data by clusters (using some features of the data). Now I want to know how many data are labeled as 0/1 in each cluster. For example, assume that I have 9 labeled data grouped into three clusters. The ids of the clusters are 1, 2, and 3. The dataset is

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 save the cluster centers to a file [1]. This first data set is huge, it is guarantied that cluster

Hi, > [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Corey Powell > > Do you know of any references that verify the accuracy of R > for basic statistical calculations and tests. The results of > these studies should indicate that R results are the same as > the results of other statistical packages to a certain number > of decimal places on some benchmark

Hi, I am using fanny and I have estrange results. I am wondering if someone out there can help me understand why this happens. First of all in most of my tries, it gives me a result in which each object has equal membership in all clusters. I have read that that means "the clustering is entirely fuzzy". Looking at the graphics it is really difficult to understand how objects with so

Hi, I've lately been wondering where benchmarks for LLVM-generated binaries are hosted, and whether they're tracked over time. I'm asking because I'm thinking of where to put some benchmarks I've written using the open source Google benchmarking library [0] to test certain costs of XRay-instrumented binaries, the XRay runtime, and other related measurements (effect of

I've made a package from some benchmark datasets for use with R and uploaded it to CRAN. Here's the Index entry: mlbench-0.1.tar.gz: A collection of artificial and real-world machine learning benchmark problems, including, e.g., the boston housing data from the UCI repository. Written/packaged by Fritz Leisch <Friedrich.Leisch at ci.tuwien.ac.at> Original data sets from

I've made a package from some benchmark datasets for use with R and uploaded it to CRAN. Here's the Index entry: mlbench-0.1.tar.gz: A collection of artificial and real-world machine learning benchmark problems, including, e.g., the boston housing data from the UCI repository. Written/packaged by Fritz Leisch <Friedrich.Leisch at ci.tuwien.ac.at> Original data sets from

I've made a package from some benchmark datasets for use with R and uploaded it to CRAN. Here's the Index entry: mlbench-0.1.tar.gz: A collection of artificial and real-world machine learning benchmark problems, including, e.g., the boston housing data from the UCI repository. Written/packaged by Fritz Leisch <Friedrich.Leisch at ci.tuwien.ac.at> Original data sets from

Hi, all, I?d like to do the clustering analysis in my dataset. The example data are as follows: Dataset 1: 500, 490, 486, 490, 491, 493, 480, 461, 504, 476, 434, 500, 470, 495, 3116, 3142, 12836, 3062, 3091, 3141, 3177, 3150, 3114, 3149; Dataset 2: 506, 473, 495, 494, 434, 459, 445, 475, 476, 128367, 470, 513, 466, 476,482, 1201, 469, 502; I had so many datasets like that. Basically, every

Dear R Users, I am attempting to use the snow package for clustering. Is there a way to identfy, in the environment of each node, a rank for that node and also, the total size of the cluster ? By way of analogy, I am looking for the functions in snow equivalent to mpi.comm.rank() and mpi.comm.size() from RMPI, in case that makes things clearer. Thanks in advance, Tolga Generally, this

Duncan, I have some issues with representing this as a single "fast" mode flag, which mostly boil down to the fact that this is a very C-centric view of the world. And, since C compilers are not generally known for their awesomeness on issues of numerics, I'm not sure that's a good idea. Having something called a "fast" or "relaxed" mode implies that it is

Hi Berend, I see you are one of the contributors to the rbecnhmark package. I am sorry that I am bothering you again. I have tried to run your code (slightly tweaked) involving the benchmark function, and I am getting the following error message. What am I doing wrong? Error in benchmark(d1 <- s1(df), d2 <- s2(df), d3 <- s3(df), d4 <- s4(df), : could not find function

hi r-help, i am doing kmeans clustering in stats. i tried for five clusters clustering using: kcl1 <- kmeans(as1[,c("contlife","somlife","agglife","sexlife",                         "rellife","hordlife","doutlife","symtlife","washlife",                       

Hi: I'm clustering a microarray dataset with a large # of samples. I would like your opinion on the best way to automatically determine the optimal # of clusters. Currently I am using the "cluster" package, clustering with "clara", examining the average silhouette width at various numbers of clusters. I'd like opinions on whether any newer packages offer

I have a directed graph which is represented as a matrix on the form 0 4 0 1 6 0 0 0 0 1 0 5 0 0 4 0 Each row correspond to an author (A, B, C, D) and the values says how many times this author have cited the other authors. Hence the first row says that author A have cited author B four times and author D one time. Thus the matrix represents two groups of authors: (A,B) and (C,D) who cites

Hi Owen, > I have some issues with representing this as a single "fast" mode flag, it isn't a single flag, that's the whole point of using metadata. OK, right now there is only one option (the "accuracy"), true, but the intent is that others will be added, and the meaning of accuracy tightened, later. MDBuilder has a createFastFPMath method which is intended to