Details
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New Feature
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Status: Open
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Major
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Resolution: Unresolved
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Description
MiniBatchKMeans is a fast clustering algorithm,
which use partial points in initialize cluster centers, and mini batch in training iterations.
It can finish in few seconds on clustering millions of data, and has few differences between KMeans.
I have implemented it by Kotlin in my own project, and I'd like to contribute the code to Apache Commons Math, of course in java.
My implemention is base on Apache Commons Math3, refer to Python sklearn.cluster.MiniBatchKMeans
Thought test I found it works well on intensive data, significant performance improvement and return value has few difference to KMeans++, but has many difference on sparse data.
Below is the comparation of my implemention and KMeansPlusPlusClusterer
I have created a pull request on https://github.com/apache/commons-math/pull/117, for reference only.
