similar to: R code for OptiGrid Clustering

Dear R-users, I have a two - dimensional data set which needs to be clustered into groups: I'm searching for groups of points which show a positive correlation (in a twodimensional plot of the data set), but I do not have any knowledge about how many groups there might be. Do you know of a clustering algorithm in R (or in general) which can use a-priori information about the cluster's

Hello sir, You have interpreted correctly that clustering will be done by generating the ring around the Document(i.e. the basic idea of LSI). But it is not like increasing the radius and the next shell will be another cluster, Rather it would pick one document (based on relevance score) and form a ring around it to cluster the document, then from the remaining documents(not in the cluster but

Dear R-Users, I'm a master student in Mathematics and Statistics at Carleton University, Ottawa, Canada. I'm studying Clustering methods including different related algorithms. One of them is Support Vector Clustering algorithm. I was wondering whether anybody implemented this algorithm and could help me with the S-Plus or R computer code that I could use in my simulations. I would

I am still trying to find some faster clustering technique for search result. One technique which strike to me is, using the Latent Semantic Indexing for Clustering the search result can give better results. In which we don't even need to iterate over different values of 'k'(in K-means algorithm) to cluster documents rather we can cluster whole search result in one go. How Latent

Dear All, I hope I am not asking a FAQ. I am dealing with a problem of graph theory [connected components in a non-directed graph] and I do not want to rediscover the wheel. I saw a large number of R packages dealing for instance with the k-means method or hierarchical clustering for spatially distributed data and I am basically facing a similar problem. I am given a set of data which are the

On Sat, Mar 12, 2016 at 04:27:55PM +0530, Richhiey Thomas wrote: > Below I write a raw version of my proposal for Clustering of Search Results > based on our previous mails. Hi, Richhiey. Thanks for putting this together ahead of the formal start of applications, and sharing it with us -- and it's really not too long! Project proposals for something that will last the summer are

On Mon, Mar 10, 2014 at 3:59 PM, Olly Betts <olly at survex.com> wrote: > Exactly what approach the project takes isn't nailed down - it just > seemed something which would be interesting for a student to work on, > and would be useful to Xapian users. > > My understanding of the current clustering branch (which may not be > completely accurate) is that it clusters

Dear WizaRds, My goal is to program the VS-KM algorithm by Brusco and Cradit 01 and I have come to a complete stop in my efforts. Maybe anybody is willing to follow my thoughts and offer some help. In a first step, I want to use a single variable for the partitioning process. As the center-matrix I use the objects that belong to the cluster I found with the hierarchial Ward algorithm. Then,

Sir, I have prepared a draft proposal for clustering problem. If you have previous year's accepted proposal please share it, that would be very helpful. And also I have attached my proposal, please review it. Thanks On Mon, Mar 21, 2016 at 7:10 PM, James Aylett <james-xapian at tartarus.org> wrote: > On Sun, Mar 20, 2016 at 11:39:37PM +0530, MURTUZA BOHRA wrote: > > >

2014-03-11 8:47 GMT+08:00 Olly Betts <olly at survex.com> wrote: > Most applications of Xapian are interactive, so to actually be > practically useful clustering needs to complete in a reasonable amount > of time (a fraction of a second ideally). I think that needs to be a key > aim of the project. > If by "find new approaches" you mean different approach to that

I am currently designing a clustering algorithm in collaboration with one of my colleagues. For comparison purposes we would like to contrast it with the Support Vector Clustering algorithm of (A. Ben-Hur, D. Horn, H.T. Siegelmann, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125-137, 2001). This is supposedly the most powerful unsupervised clustering

Hello devs. I would like to propose how I plan to go about improving and getting a system that can be integrated into Xapian in this GSoC for the clustering branch. I have identified three areas of work which were not touched last time. 1) Automated Performance Analysis I had roughly implemented 2 evaluation techniques previously (Distance b/w document and centroids within clusters and

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 Dr. Liaw Andy: I have a few more questions about your heatmap function. actually heatmap is what I am looking for. heatmap(x, Rowv, Colv, distfun = dist, hclustfun = hclust, add.expr, scale=c("row", "column", "none"), na.rm = TRUE, ...) my data is a XNEW, > dim(XNEW) [1] 554 335 554 genes, 335 samples. now I want to use 1-CORR as a distance

Hello sir! Thank you for your gentle reply, I have visited to the github repository before but the code was not commented properly still I am trying to understand and figure out the redundant task in the algorithm by reading the code. For the same I asked you if there is any other document or information is available with you which can help me to understand it better would be great. It's okay

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

Hello, I have submitted my proposal on GSoC. But I have little idea about the timeline. Many things are difficult to be determined. Cheers, Liu Chi 2014-03-11 21:33 GMT+08:00 Olly Betts <olly at survex.com>: > On Tue, Mar 11, 2014 at 10:11:31AM +0800, Chi Liu wrote: > > Thank you for your patient explanation about the project. My > > understanding about the project

Hi, I would like to know if someone knows an extended implementation of k-means in R to find appropriate number of clusters for a given k-dimensional data. Also, I am working on clustering for forecasting, if someone is interested or has knowledge on implementational details please mail me, I would appreciate it. Regards Skanda Kallur "Cogito, ergo sum" (I think, therefore I

Hello everyone, I'm new to this list, so let me introduce myself: my name is Jonck van der Kogel and I am a graduate student at the Erasmus University of Rotterdam. I am currently working on my thesis which is on the use of artificial intelligence for large data-sets. To do an analysis of a certain data-set I want to use Kohonen's SOM algorithm. However, as I understand it, the SOM