cluster-analysis

Let's say I have a text transcript of a dialogue over a period of aprox. 1 hour. I want to know what words happen in close proximatey to one another. What type of statistical technique would I use to determine what words are clustered together and how close their proximatey to one another is? I'm suspecting some sort of cluster analysis or PCA. jayunit100 To determine word proximity, you will have to build a graph: each word is a vertex (or "node"), and left and right words are edges So "I like dogs" would have 2 edges and 3 vertices. Now, the next step will be to decide based on this model

There seems to be a lot of information about creating either hierarchical or k-means clusters. But I would like to know if there is an solution in R that would create K clusters of approximately equal sizes. There is some stuff out there about doing this in other languages, but I have not been able to find anything from searching on the internet that suggests how to achieve the result in R. An example would be set.seed(123) df <- matrix(rnorm(100*5), nrow=100) km <- kmeans(df, 10) print(sapply(1:10, function(n) sum(km$cluster==n))) which results in [1] 14 12 4 13 16 6 8 7 13 7 I would ideally

How can one generate say 1000 random points with a distribution like that of towns and cities in e.g. Ohio ? I'm afraid I can't define "distributed like cities" precisely; uniformly distributed centres + small Gaussian clouds are easy but ad hoc. Added: There must be a family of 2d distributions with a clustering parameter that can be varied to match a given set of points ? Maybe you can take a look at Walter Christaller's Theory of Central Places . I guess there must be some generator somewhere, or you can cook up your own. Start with a model of the water features in your target area (or make

I can't seam to find any simple enough tutorials or descriptions on clustering in scipy, so I'll try to explain my problem: I try to cluster documents (hierarchical agglomerative clustering) , and have created a vector for each document and produced a symmetric distance matrix. The vector_list contains (really long) vectors representing each document. The order of this list of vectors is the same as my list of input documents so that I'll (hopefully) be able to match the results of the clustering with the corresponding document. distances = distance.cdist(vector_list, vector_list, 'euclidean')

I am trying to plot the elbow of k means using the below code: load CSDmat %mydata for k = 2:20 opts = statset('MaxIter', 500, 'Display', 'off'); [IDX1,C1,sumd1,D1] = kmeans(CSDmat,k,'Replicates',5,'options',opts,'distance','correlation');% kmeans matlab [yy,ii] = min(D1'); %% assign points to nearest center distort = 0; distort_across = 0; clear clusts; for nn=1:k I = find(ii==nn); %% indices of points in cluster nn J = find(ii~=nn); %% indices of points not in cluster nn clusts{nn} = I; %% save into clusts cell array if (length(I)>0) mu(nn,:) = mean(CSDmat(I,:)); %% update mean %% Compute

I'm using Opencv's K-means implementation to cluster a large set of 8-dimensional vectors. They cluster fine, but I can't find any way to see the prototypes created by the clustering process. Is this even possible? OpenCV only seems to give access to the cluster indexes (or labels). If not I guess it'll be time to make my own implementation! I can't say I used OpenCV's implementation of Kmeans, but if you have access to the labels given to each instance, you can simply get the centroids by calculating the average vector of instances belong to each of the clusters. As of (at least) OpenCV 2.0,