This project seeks to systematically benchmark and compare k-means implementations across the following aspects:
- Software ecosystem: R (e.g.,
stats,ClusterR) vs Julia (e.g.,Clustering) - Algorithm variants: Variants like Lloyd’s, Hartigan-Wong
- Initialization: Random seeding, k-means++
We evaluate the performance from three main metrics:
- Clustering accuracy
- Ratio of the Between-sum-of-squares / Total-sum-of-squares
- Computational time
💫 You can check the interactive Plotly figures at https://hohoweiya.xyz/KmeansBenchmarks.jl
This work aims to provide actionable insights for researchers and practitioners in selecting optimal k-means configurations tailored to their data size, dimensionality, and domain requirements.