Hierarchical data organization, clustering and denoising via localized diffusion folders

DOI10.1016/j.acha.2011.09.002zbMath1239.68060OpenAlexW2014569347WikidataQ110124941 ScholiaQ110124941MaRDI QIDQ427062

Gil David, Amir Z. Averbuch

Publication date: 13 June 2012

Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.acha.2011.09.002

zbMATH Keywords

hierarchical clusteringdiffusion geometryspectral graph

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (10)

Diffusion-based kernel methods on Euclidean metric measure spaces ⋮ Learning from patches by efficient spectral decomposition of a structured kernel ⋮ Updating kernel methods in spectral decomposition by affinity perturbations ⋮ Coarse-grained localized diffusion ⋮ Diffusion representations ⋮ Polar Classification of Nominal Data ⋮ Diffusion nets ⋮ A class of Laplacian multiwavelets bases for high-dimensional data ⋮ Parameter Rating by Diffusion Gradient ⋮ A filtering technique for Markov chains with applications to spectral embedding

Uses Software

• UCI-ml
• SpectralCAT
• clusfind

Cites Work

• Orientability and diffusion maps
• SpectralCAT: categorical spectral clustering of numerical and nominal data
• Cure: An efficient clustering algorithm for large databases
• Diffusion maps
• Finding Groups in Data
• Neural networks for nonlinear dynamic system modelling and identification
• Data Clustering: Theory, Algorithms, and Applications
• Advances in Intelligent Data Analysis VI
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