Implementation of basic k-means clustering algorithm using Rust. Pseudocode:
k_means(D, k, eps):
# Randomly initialize centroids (c_1(t), ... , c_k(t))
repeat:
t <- t + 1
C_i <- ∅
// Cluster update step:
∀i ∈ [1:k]: C_i = { x ∈ D | d(x, c_i(t - 1)) = min_j d(x, c_j(t - 1)) }
// Centroid update step:
c_i(t) := centroid(C_i)
until sum_i^k d(c_i(t), c_(t-1)) < eps
Here centroid(C) is the sum of all vectors in D divided by
the cardinality of D (center of mass).
From within the project's root folder run:
cargo run -- -i example/dataset.csv -o output -k 3
