A hybrid algorithm with cluster analysis in modelling high dimensional data

DOI10.1016/j.dam.2017.09.002zbMath1383.62179OpenAlexW2761327372MaRDI QIDQ1682890

Burcu Tunga

Publication date: 6 December 2017

Published in: Discrete Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.dam.2017.09.002

zbMATH Keywords

approximationdata modellingcluster analysishigh-dimensional problemsEMPR

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (1)

A unified framework for bivariate clustering and regression problems via mixed-integer linear programming

Uses Software

• MuPAD
• PERL
• WEKA
• AS 136

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• A novel piecewise multivariate function approximation method via universal matrix representation
• A novel method for multivariate data modelling: Piecewise Generalized EMPR
• Reductive enhanced multivariance product representation for multi-way arrays
• The influence of the support functions on the quality of enhanced multivariance product representation
• Optimising sum-of-squares measures for clustering multisets defined over a metric space
• Balanced compact clustering for efficient range queries in metric spaces
• A new approach for multivariate data modelling in orthogonal geometry
• Least squares quantization in PCM
• Multidimensional partitioning and bi-partitioning: analysis and application to gene expression data sets
• Multivariate quasi-interpolation inL^p(ℝ^d) with radial basis functions for scattered data

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