Clustering a long list of strings (words) into similarity groups
$\begingroup$ 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, if you get to learn clustering branch as it is you'll find that there exist no …
What are the most common metrics for comparing two clustering ...
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 a case where a very coarse classification was used (large classes), and the other clustering algorithms just produced more fine-grained result ...
Pros and cons of clustering algorithms? - Cross Validated
Well, Weka does not have very many clustering algorithms. It's a classification and machine learning tool, not so much a general purpose data mining tool (I would not call "clustering" and "data mining" to be part of "machine learning" - they don't learn!). There is for example ELKI which has a lot more clustering and outlier detection methods.
Clustering methods that do not require pre-specifying the number …
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 summarize; it's a bit like asking for a description of any organisms that aren't cats. Clustering algorithms are often categorized into broad kingdoms:
clustering - What algorithm should I use to cluster a huge binary ...
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. kmeans is simple and clustering 650K vectors should be easily feasible on a decent desktop.
clustering - Books on cluster algorithms - Cross Validated
2013年4月29日 · I have checked: Data clustering: theory, algorithms, and applications. Data mining: concepts, models, methods and algorithms and Cluster Analysis, 5th edition. I don't need no padding, just a few books in which the algorithms are well described, with their pros and cons.
Use of bootstrap in clustering algorithms - Cross Validated
2014年12月24日 · Are there clustering algorithms that take advantage of bootstrap? For example can one combine bootstrap with a standard K-Means algorithm to scale K-Means. I was thinking if the following at a high-level: Use bootstrap to create sub-samples from the elements that need to be clustered. Each of these sub-samples should be the size that K-Means scale
r - Methods for comparing clustering results - Cross Validated
You can use the Adjusted Rand Index or the Adjusted Mutual Information to measure the similarity (agreement) of the overall results of two clustering algorithms on an overlapping dataset. Both scores are adjusted for chance which means that 2 random clusterings will likely have an ARI or AMI close to 0.0.
Is it important to scale data before clustering?
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 to do a PCA, then normalise, but that would only help if the clusters are linearly separable and don't overlap in the PCA dimensions.
Are there any non-distance based clustering algorithms?
2016年3月10日 · Density-based clustering like DBSCAN has a different name, and isn't focused around minimizing distances; but "density" is usually specified with respect to a distance, so technically these algorithms are either distance-based or grid-based.