Has anyone made a manual syntax of hierarchical clustering or ward clustering without the function (hclust() or agnes()) that found in packages ('cluster') in R?
If you have, can you share it?
Thanks a lot for the help
Related
I'm wondering if ther is a package to plot the feasible region (based on the constraints) and the optimal solution for linear programming in Julia Language using Juno. Do you know a way to do this? Suppose we have a LP problem and want to visualize the region and optimal solution. Is there a package to do this? How can I do this. I would appreciate so much your help. Thanks.
I am looking for R packages for hierarchical clustering (or other clustering methods) that can handle mixed data types. I have a data set with continuous and ordinal variables.
Any recommendations are greatly appreciated.
I am using FactoMineR. This can handle mixed data easily and is well documented.
Damien
I'm trying to make a hierarchical clustering tree from the clustering produced by Seurat's clustering function. Their functions, BuildClusterTree and PlotClusterTree, produce a frankly ugly tree based on SNN (shared nearest neighbor) algorithm that you can't manipulate with ggplot2. I'm trying to figure out how to use other functions to plot the clustering already produced by Seurat, but I can't figure out how or what R package would work best. Does anyone have any advice for me?
I would suggest the clustree package for this purpose
I performed K-means clustering in R to profile customers with different spending behaviours. Now that I got the clusters and want to get the ANOVA table indicates which variables contribute the most to my cluster solution in R. How can I do so?
I've seen that this can be done in SPSS but not so sure how to do it in R.
Here is a tutorial on how this can be done in SPSS: https://arifkamarbafadal.files.wordpress.com/2011/09/ebook-037-tutorial-spss-k-means-cluster-analysis.pdf
Appreciate any help on this!
I currently have an adjacency matrix I would like to perform spectral clustering on to determine the community each node belongs to. I have looked around, but there do not look to be implementations in either igraph or other packages.
Another issue is determining how many clusters you want. I was wondering if R has any packages that might help one find the optimal number of clusters to break an adjacency matrix into? Thanks.
I cannot advise for R, however, I can suggest this example implementation of Spectral Clustering using Python and Networkx (which is comparable to iGraph). It should not be hard to translate this into R.
For an introduction to Spectral Clustering see lectures 28-34 here and this paper.