A novel fast heuristic to handle large-scale shape clustering

DOI10.1080/00949655.2014.1000900OpenAlexW2155412316MaRDI QIDQ5222322

Hassan Ismkhan

Publication date: 1 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2014.1000900

zbMATH Keywords

heuristicsclusteringautomatic cluster numberlarge-scale shape clustering

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Determining the number of clusters using information entropy for mixed data
• Fast global \(k\)-means clustering based on local geometrical information
• Fast modified global \(k\)-means algorithm for incremental cluster construction
• Modified global \(k\)-means algorithm for minimum sum-of-squares clustering problems
• A multi-prototype clustering algorithm
• A distance-relatedness dynamic model for clustering high dimensional data of arbitrary shapes and densities
• Shape clustering: common structure discovery
• A fast \(k\)-means clustering algorithm using cluster center displacement
• Robust path-based spectral clustering
• Adaptive evolutionary clustering
• A graph-theoretical clustering method based on two rounds of minimum spanning trees
• Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters

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