josh.meanings.initializations.mc2 — org.clojars.joshua/josh.meanings 0.3.2

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.

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.

t-chain-length clj

t-cluster-count clj

t-config clj

t-dataset clj

t-datasets clj

t-point clj

t-points clj