Book Chapter10.1016/B978-0-444-87137-4.50025-4
Mapping Techniques for Exploratory Pattern Analysis
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TL;DR: A versatile collection of mapping methods for computer-aided pattern analysis and the least squares mapping, which combines a squared error criterion with agglomerative hierarchical clustering, are described.
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Abstract: We describe a versatile collection of mapping methods for computer-aided pattern analysis, and we report the results of two experiments: one for cluster analysis and the second for classifier design. The first experiment involved sixteen human subjects and the second involved fourteen. The collection of mapping methods includes our innovation — the least squares mapping, which combines a squared error criterion with agglomerative hierarchical clustering. In both of these experiments untrained humans aided by the generalized declustering mapping and our least squares mapping outperformed or equaled automatic clustering and classifier design techniques.
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References
A Nonlinear Mapping for Data Structure Analysis
TL;DR: An algorithm for the analysis of multivariate data is presented along with some experimental results that is based upon a point mapping of N L-dimensional vectors from the L-space to a lower-dimensional space such that the inherent data "structure" is approximately preserved.
3.7K
A Projection Pursuit Algorithm for Exploratory Data Analysis
Jerome H. Friedman,John W. Tukey +1 more
TL;DR: An algorithm for the analysis of multivariate data is presented and is discussed in terms of specific examples to find one-and two-dimensional linear projections of multivariable data that are relatively highly revealing.
A Triangulation Method for the Sequential Mapping of Points from N-Space to Two-Space
TL;DR: A method for the sequential mapping of points in a high-dimensional space onto a plane is presented, where whenever a new point is mapped, its distgnces to two points previously mapped are exactly preserved.
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