clustering - What algorithm should I use to cluster a huge binary ...
No need to use a specific binary clustering algorithm. kmeans is simple and clustering 650K vectors should be easily feasible on a decent desktop. 4 - If you wish to have binary cluster …
Clustering a long list of strings (words) into similarity groups
6 Use graph clustering algorithms, such as Louvain clustering, Restricted Neighbourhood Search Clustering (RNSC), Affinity Propgation Clustering (APC), or the Markov Cluster algorithm (MCL).
Choosing a clustering method - Cross Validated
Because of the strong non-linear cluster in the second data set, the linkage and density clustering algorithms work far better than any centroid-based method. There is no similarity measure that …
Is it important to scale data before clustering? - Cross Validated
Mar 12, 2014 · I found this tutorial, which suggests that you should run the scale function on features before clustering (I believe that it converts data to z-scores). I'm wondering whether …
Clustering algorithms for extremely sparse data
I am trying to cluster an extremely sparse text corpus, and I know the number of clusters (my data is the title and author list of scientific publications, for which I already know the number of
Applying clustering algorithms after t-SNE in R - Cross Validated
Apr 2, 2024 · So I'm doing my bachelor`s work and I'm applying different clustering algorithms on certain data. Before all the clustering of course I'm using a dimensionality reduction algorithm …
Clustering methods that do not require pre-specifying the number …
Oct 24, 2016 · Are there any "non-parametric" clustering methods for which we don't need to specify the number of clusters? And other parameters like the number of points per cluster, etc.
Evaluation measures of goodness or validity of clustering (without ...
Jan 27, 2012 · Internal indices are used to measure the goodness of a clustering structure without external information (Tseng et al., 2005). For external indices, we evaluate the results of a …
Deterministic clustering approaches - Cross Validated
Jun 19, 2016 · A family of clustering algorithms which satisfies your conditions is Spectral Clustering. They are batch algorithms and will give you the same results for the same …
What are the most common metrics for comparing two clustering ...
For clustering results, usually people compare different methods over a set of datasets which readers can see the clusters with their own eyes, and get the differences between different …