josh.meanings.initializations.mc2

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make-weight-fn
t-chain-length
t-cluster-count
t-config
t-dataset
t-datasets
t-point
t-points

Approximating K-Means in sublinear time is a paper written by Olivier Bachmem, Mario Lucic, Hamed Hassani, and Andreas Krause which shares a method for obtaining a provably good approximation of k-means++ in sublinear time. The method they share uses markov chain monte carlo sampling in order to approximate the D^2 sampling that is used in k-means++. Since this method is proven to converge to drawing from the same distribution as D^2 sampling in k-means++ the theoretical competitiveness guarantees of k-means++ are inherited. This algorithm is sublinear with respect to input size which makes it different from other variants of k-means++ like k-means||. Whereas a variant like k-means|| allows for a distributed k-means++ computation to be carried out across a cluster of computers, k-means-mc++ is better suited to running on a single machine.

Approximating K-Means in sublinear time is a paper written by 
Olivier Bachmem, Mario Lucic, Hamed Hassani, and Andreas Krause 
which shares a method for obtaining a provably good approximation
of k-means++ in sublinear time. The method they share uses markov 
chain monte carlo sampling in order to approximate the D^2 sampling 
that is used in k-means++. Since this method is proven to converge to 
drawing from the same distribution as D^2 sampling in k-means++ the 
theoretical competitiveness guarantees of k-means++ are inherited. 
This algorithm is sublinear with respect to input size which makes 
it different from other variants of k-means++ like k-means||. Whereas
a variant like k-means|| allows for a distributed k-means++ computation 
to be carried out across a cluster of computers, k-means-mc++ is 
better suited to running on a single machine.

raw docstring

make-weight-fn^clj

(make-weight-fn distance-fn clusters)

Create a function which computes the weight of a point given the current set of clusters.

Create a function which computes the weight of a point given the 
current set of clusters.

source raw docstring

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josh.meanings.initializations.mc2

make-weight-fnclj

t-chain-lengthclj

t-cluster-countclj

t-configclj

t-datasetclj

t-datasetsclj

t-pointclj

t-pointsclj

make-weight-fn^clj

t-chain-length^clj

t-cluster-count^clj

t-config^clj

t-dataset^clj

t-datasets^clj

t-point^clj

t-points^clj