I ran a clustering test on crawled pages (more than 25K docs ; personal data set). I\'ve done a clusterdump :
I\'ve created a codebook using k-means of size 4000x300 (4000 centroids, each with 300 features). Using the codebook, I then want to label an input vector (for purposes of binning later on). The input
l开发者_运维知识库ibrary(amap) set.seed(5) Kmeans(mydata, 5, iter.max=500, nstart=1, method=\"euclidean\")
I am using R software (R comm开发者_如何学编程ander) to cluster my data. I have a smaller subset of my data containing 200 rows and about 800 columns. I am getting the following error when trying kmea
I tried both kmeans() and kmeansCBI() from the fps package on my dataset. But, they give different SSE value, so I don\'t know which one is correct value.
I want to run Mahout\'s K-Means example in a hadoop cluster of 5 machines. Which Mahout jar files should I need to keep in all the nodes, in order for the K-Means to be exec开发者_如何学运维uted in a
开发者_如何学运维Is it possible to specify your own distance function using scikit-learn K-Means Clustering?Here\'s a small kmeans that uses any of the 20-odd distances in
I\'m using OpenCV\'s python interface to do K-Means clustering of multidimensional data (usually dimension of 7). I\'m getting strange
I\'m trying to cluster a set of 4D vectors, without knowing how many clusters there should be in advance. In the past, I\'ve been able to use cvKmeans2 to cluster, given knowledge of the number of clu
I have a simple machine learning question: I have n (~110) elements, and a matrix of all the pairwise distances. I would like to choose the 10 elements that are most far apart. That is, I want to
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