$\begingroup$ It seems that there are some special string clustering algorithms. If you come from specifically text-mining field, not statistics /data analysis, this statement is warranted. However, …

2016年10月24日 · Clustering algorithms that require you to pre-specify the number of clusters are a small minority. There are a huge number of algorithms that don't. They are hard to …

This is still not straightforward -- a clustering can be consistent with a a gold standard (be a sub-clustering or super-clustering) and this has to be taken into account but often is not. I've seen …

The L Method is described here: Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms Stan Salvador and Philip Chan. Essentially this evaluates …

Clustering algorithms can be categorized based on their "cluster model". An algorithm designed for a particular kind of model will generally fail on a different kind of model. For eg, k-means …

Other clustering algorithms with better features tend to be more expensive. In this case, k-means becomes a great solution for pre-clustering, reducing the space into disjoint smaller sub …

2014年3月12日 · Clustering on the normalised data works very well. The same would apply with data clustered in both dimensions, but normalisation would help less. In that case, it might help …

3 - Now apply kmeans. you can use the Euclidean metric, or experiment with other metrics that you kmeans implementation supports. No need to use a specific binary clustering algorithm. …

2012年1月27日 · Most often, internal clustering criteria are used for comparing cluster partitions with different number of clusters k obtained via the same method of clustering (or other …

I am conducting clustering analysis in which I am using three clustering algorithms K-means, Spectral Clustering, and Hierarchical clustering on 3 datasets in UCI repository. I have used R …