A new algorithm for nonlinear mapping with applications to dimension and cluster analyses

DOI10.1016/0031-3203(83)90013-4zbMath0504.62055OpenAlexW2057050699MaRDI QIDQ1836242

Osamu Kakusho, Riichiro Mizoguchi

Publication date: 1983

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0031-3203(83)90013-4

zbMATH Keywords

cluster analysisk-nearest neighbornew algorithmminimum dimensionnonlinear mapping methodnonmetric multidimensional scaling of Kruskal type

Mathematics Subject Classification ID

Software, source code, etc. for problems pertaining to statistics (62-04) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (1)

Computer-aided thinking by mapping text-objects into metric spaces

Cites Work

• The percolation method for an efficient grouping of data
• The analysis of proximities: Multidimensional scaling with an unknown distance function: I, II
• Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
• An Intrinsic Dimensionality Estimator from Near-Neighbor Information
• Printer graphics for clustering
• An Algorithm for Finding Intrinsic Dimensionality of Data

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