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How can you compare two clusters in machine learning?
I run KMeans clustering on this data and get 2 clusters [ (A,B), (C)]. Then I run MeanShift clustering on this data and get 2 clusters [ (A), (B,C)]. So clearly the two clustering methods have clustered the data in different ways. I want to be able to quantify this difference.
When to compare a new clustering algorithm to an existing method?
When it comes to compare a new clustering algorithm, one always wants to show the advantages of his/her method over existing and well known methods. Going this way may mislead one to ignore disadvantages proposed method.
What kind of metrics are used for 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 methods results. There are some metrics, like Homogeneity, Completeness, Adjusted Rand Index, Adjusted Mutual Information, and V-Measure.
Can a clustering be consistent with a gold standard?
(See e.g. my answers here: Comparing clusterings: Rand Index vs Variation of Information and here: Forgiving measure for external cluster validation ). 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.
Are there any good examples of cluster algorithms?
While these examples give some intuition about the algorithms, this intuition might not apply to very high dimensional data. /home/circleci/project/sklearn/cluster/_affinity_propagation.py:148: FutureWarning: ‘random_state’ has been introduced in 0.23.
How to plot a cluster in scikit learn?
Click here to download the full example code or to run this example in your browser via Binder This example shows characteristics of different clustering algorithms on datasets that are “interesting” but still in 2D.
Which is the best algorithm for affinity propagation?
/home/circleci/project/sklearn/cluster/_affinity_propagation.py:148: FutureWarning: ‘random_state’ has been introduced in 0.23. It will be set to None starting from 1.0 (renaming of 0.25) which means that results will differ at every function call.