cluster-analysis

The problem comes as follows. I have M images and extract N features for each image, and the dimensionality of each feature is L. Thus, I have M*N features (2,000,000 for my case) and each feature has L dimensionality (100 for my case). I need to cluster these M*N features into K clusters. How can I do it? Thanks. Do you want 1000 clusters of images, or of features, or of (image, feature) pairs ? In any case, it sounds as though you'll have to reduce the data and use simpler methods. One possibility is two-pass K-cluster: a) split the 2 million data points into 32 clusters, b) split each of

I want to cluster people into groups based on their interests. For eg. people who like machine learning and graphs may be placed in a group and people who have interest in mathematics and economics etc. may be placed in a different group. The algorithm should be able to decide which people have most matching interests based on the interests of the people and create clusters.It should also be able to output about other persons in the group in which a particular person is placed. This does not sound like a particularly difficult clustering problem, and any of the off-the-shelf clustering

Is there any kind of subspace clustering packages available in scikit-learn. 来源： https://stackoverflow.com/questions/33483160/is-there-any-kind-of-subspace-clustering-package-available-in-scikit-learn

I am trying to create circular phylogenetic tree. I have this part of code: fit<- hclust(dist(Data[,-4]), method = "complete", members = NULL) nclus= 3 color=c('red','blue','green') color_list=rep(color,nclus/length(color)) clus=cutree(fit,nclus) plot(as.phylo(fit),type='fan',tip.color=color_list[clus],label.offset=0.2,no.margin=TRUE, cex=0.70, show.node.label = TRUE) And this is result: Also I am trying to show label for each node and to color branches. Any suggestion how to do that? Thanks! When you say "color branches" I assume you mean color the edges. This seems to work, but I have to