cluster-analysis

I'm doing a cluster problem, and the proxy package in R provides both dist and simil functions. For my purpose I need a distance matrix, so I initially used dist, and here's the code: distanceMatrix <- dist(dfm[,-1], method='Pearson') clusters <- hclust(distanceMatrix) clusters$labels <- dfm[,1]#colnames(dfm)[-1] plot(clusters, labels=clusters$labels) But after I ploted the image I found that the cluster result is not the way I expecte it to be, since I know what it should look like. So I tried simil instead, and the code is like: distanceMatrix <- simil(dfm[,-1], method='Pearson') clusters <-

I'm having trouble getting 'choice' to create output. When the graphical interface launches, I am selecting a partition with the space bar. This creates a black circle around the partition, indicating it has been selected. When I click 'return', nothing happens. I checked my working directory to look for the output files, but they are not there. I used getwd() to ensure that I have the correct setwd(). No dice. There was a similar question posted: Exporting result from kml package in R ; however, the answer does not work for me. Any suggestions? I am using R 3.1.0 GUI Mavericks build(6734) and

I am working on a clustering problem and it has to be scalable for a lot of data. I would like to try hierarchical clustering in Spark and compare my results with other methods. I have done some research on the web about using hierarchical clustering with Spark but haven't found any promising information. If anyone has some insight about it, I would be very grateful. Thank you. Gabe Church The Bisecting Kmeans Approach Seems to do a decent job, and runs quite fast in terms of performance. Here is a sample code I wrote for utilizing the Bisecting-Kmeans algorithm in Spark (scala) to get cluster

I am trying to plot GIS coordinates, specifically UK national Grid Coordinates which eastings and northings ressemble: 194630000 562220000 I can plot these using clusplot in the Cluster library: clusplot (df2,k.means.fit$cluster,main=i,color=TRUE,shade=FALSE,labels=0,lines=0,bty="7") where df2 is my data frame and k.means.fit is the result of the K means analysis on df2. Note that the coordinates of the centers after the k means analysis have not been normalised: k.means.fit$centers # Grid.Ref.Northing Grid.Ref.Easting #1 206228234 581240726 But when I plot the clusters, all the points are

I have created my Vectors as described in this question and have run mahout kmeans on the data. Since I'm using Mahout 0.7, the clusterdump command didn't work as described in Mahout in Action, but I got it to work like this: export HADOOP_CLASSPATH=/path/to/mahout-distribution-0.7/core/target/mahout-core-0.7-job.jar:/path/to/mahout-distribution-0.7/integration/target/mahout-integration-0.7.jar hadoop jar core/target/mahout-core-0.7-job.jar org.apache.mahout.utils.clustering.ClusterDumper -i /clustering/out/clusters-20-final -o textout -of TEXT and I am getting lines like this one: VL-1383471

I have an ordered 1-D array of numbers. Both the array length and the values of the numbers in the array are arbitrary. I want to partition the array into k partitions, according to the number values, e.g. let's say I want 4 partitions, distributed as 30% / 30% / 20% / 20%, i.e. the top 30% values first, the next 30% afterwards, etc. I get to choose k and the percentages of the distribution. In addition, if the same number appears more than once in the array, it should not be contained in two different partitions. This means that the distribution percentages above are not strict, but rather

I want to compute the pairwise distances of 57832 vectors. Each vector has 200 dimensions. I am using pdist to compute the distances. from scipy.spatial.distance import pdist pairwise_distances = pdist(X, 'cosine') # pdist is supposed to return a numpy array with shape (57832*57831,). However, this causes a memory error. Traceback (most recent call last): File "/home/munichong/git/DomainClassification/NameSuggestion@Verisign/classification_DMOZ/main.py", line 101, in <module> result_clustering = clf_clustering.getCVResult(shuffle) File "/home/munichong/git/DomainClassification/NameSuggestion