An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations

DOI10.1007/s10994-021-06021-7OpenAlexW3181104647MaRDI QIDQ2071340

Mike Croucher, Stephen Langdell, Eleni Vasilaki, Avgoustinos Vouros

Publication date: 28 January 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1908.09946

zbMATH Keywords

benchmarkingK-means clusteringdeterministic clustering

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• Matlab
• R
• Silhouettes
• UCI-ml
• AS 136
• Python
• dbscan
• sparcl
• LOF
• k-means++
• GitHub
• RSKC
• clusfind

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Estimating the Number of Clusters in a Data Set Via the Gap Statistic
• Algorithm AS 136: A K-Means Clustering Algorithm
• Breakdown points of affine equivariant estimators of multivariate location and covariance matrices
• Gradual model generator for single-pass clustering
• Clustering to minimize the maximum intercluster distance
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• Robust and sparse \(k\)-means clustering for high-dimensional data
• Iterative shrinking method for clustering problems
• Finding Groups in Data
• A Framework for Feature Selection in Clustering
• Determining the Number of Clusters Using the Weighted Gap Statistic

This page was built for publication: An empirical comparison between stochastic and deterministic centroid initialisation for K-means variations