Partitioning Data Sets
Published on 30. April 1995
Book
Hardback
408 pages
978-0-8218-6606-1 (ISBN)
Description
Partitioning data sets into disjoint groups is a problem arising in many domains. The theory of cluster analysis aims to find groups that are both homogeneous (entities in the same group that are similar) and well separated (entities in different groups that are dissimilar). There has been rapid expansion in the axiomatic foundations and the computational complexity of such problems and in the design and analysis of exact or heuristic algorithms to solve them. Applications have burgeoned in psychology, computer vision, target tracking, and other areas. This book contains papers presented at the workshop Partioning Data Sets held at DIMACS in April 1993. Some of the papers cover the main paradigms of the field of cluster analysis methods and algorithms. Other topics include partitioning problems arising from multitarget tracking and surveillance and from computer and human vision. The multiplicity of approaches, methods, problems, and algorithms make for lively and informative reading.
More details
Series
Language
English
Place of publication
Providence
United States
Target group
Professional and scholarly
Weight
936 gr
ISBN-13
978-0-8218-6606-1 (9780821866061)
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Schweitzer Classification
Content
Part 1. Cluster Analysis Methods: The median procedure for partitions by J.-P. Barthelemy and B. Leclerc Structural properties of pyramidal clustering by P. Bertrand Partitioning by maximum adjacency search of graphs by W. Cai and D. W. Matula From data to knowledge: Probabilist objects for a symbolic data analysis by E. Diday A labeling algorithm for minimum sum of diameters partitioning of graphs by S. Gelinas, P. Hansen, and B. Jaumard Agreement subtrees, metric and consensus for labeled binary trees by W. Goddard, E. Kubicka, G. Kubicki, and F. R. McMorris How to choose $K$ entities among N by P. Hansen, B. Jaumard, and N. Mladenovic On the classification of monotone-equivariant cluster methods by M. F. Janowitz and R. Wille Contiguity-constrained hierarchical clustering by F. D. Murtagh Part 2. Target Tracking: Image segmentation based on optimal layering for precision tracking by A. Kumar, Y. Bar-Shalom, and E. Oron Multidimensional assignments and multitarget tracking by A. B. Poore Part 3. Computer Vision: Grouping edges: An efficient Bayesian multiple hypothesis approach by I. J. Cox, J. H. Rehg, S. L. Hingorani, and M. L. Miller Finding salient convex groups by D. W. Jacobs Mixture models for optical flow computation by A. Jepson and M. J. Black Multilevel detection of stereo disparity surfaces by Y. Yang and A. L. Yuille Part 4. Human Vision: Some problems of visual shape recognition to which the application of clustering mathematics might yield some potential benefits by I. Biederman Perceptual models of small dot clusters by J. Feldman Subjective contours in early vision and beyond by B. Julesz The visual perception of surfaces, their properties, and relationships by D. Kersten and S. Madarasmi Visual computations and dot cluster by S. W. Zucker.