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Partitioning Data Sets
 
Edited by: Ingemar J. Cox NEC Research Institute, Princeton, NJ
Pierre Hansen GERARD, Montreal, PQ, Canada
Bela Julesz Rutgers University, New Brunswick, NJ
A co-publication of the AMS and DIMACS
Front Cover for Partitioning Data Sets
Available Formats:
Hardcover ISBN: 978-0-8218-6606-1
Product Code: DIMACS/19
List Price: $116.00
MAA Member Price: $104.40
AMS Member Price: $92.80
Electronic ISBN: 978-1-4704-3977-4
Product Code: DIMACS/19.E
List Price: $109.00
MAA Member Price: $98.10
AMS Member Price: $87.20
Bundle Print and Electronic Formats and Save!
This product is available for purchase as a bundle. Purchasing as a bundle enables you to save on the electronic version.
List Price: $174.00
MAA Member Price: $156.60
AMS Member Price: $139.20
Front Cover for Partitioning Data Sets
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  • Front Cover for Partitioning Data Sets
  • Back Cover for Partitioning Data Sets
Partitioning Data Sets
Edited by: Ingemar J. Cox NEC Research Institute, Princeton, NJ
Pierre Hansen GERARD, Montreal, PQ, Canada
Bela Julesz Rutgers University, New Brunswick, NJ
A co-publication of the AMS and DIMACS
Available Formats:
Hardcover ISBN:  978-0-8218-6606-1
Product Code:  DIMACS/19
List Price: $116.00
MAA Member Price: $104.40
AMS Member Price: $92.80
Electronic ISBN:  978-1-4704-3977-4
Product Code:  DIMACS/19.E
List Price: $109.00
MAA Member Price: $98.10
AMS Member Price: $87.20
Bundle Print and Electronic Formats and Save!
This product is available for purchase as a bundle. Purchasing as a bundle enables you to save on the electronic version.
List Price: $174.00
MAA Member Price: $156.60
AMS Member Price: $139.20
  • Book Details
     
     
    DIMACS - Series in Discrete Mathematics and Theoretical Computer Science
    Volume: 191995; 408 pp
    MSC: Primary 62; 68; 92;

    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.

    Readership

    This book is directed to a multidisciplinary audience who are interested in the problems associated with partitioning data.

  • Table of Contents
     
     
    • Part I. Cluster Analysis Methods
    • The median procedure for partitions
    • Structural properties of pyramidal clustering
    • Partitioning by maximum adjacency search of graphs
    • From data to knowledge: Probabilist objects for a symbolic data analysis
    • A labeling algorithm for minimum sum of diameters partitioning of graphs
    • Agreement subtrees, metric and consensus for labeled binary trees
    • How to choose K entities among N
    • On the classification of monotone-equivariant cluster methods
    • Contiguity-constrained hierarchical clustering
    • Part II. Target Tracking
    • Image segmentation based on optimal layering for precision tracking
    • Multidimensional assignments and multitarget tracking
    • Part III. Computer Vision
    • Grouping edges: An efficient Bayesian multiple hypothesis approach
    • Finding salient convex groups
    • Mixture models for optical flow computation
    • Multilevel detection of stereo disparity surfaces
    • Part IV. Human Vision
    • Some problems of visual shape recognition to which the application of clustering mathematics might yield some potential benefits
    • Perceptual models of small dot clusters
    • Subjective contours in early vision and beyond
    • The visual perception of surfaces, their properties, and relationships
    • Visual computations and dot cluster
  • Requests
     
     
    Review Copy – for reviewers who would like to review an AMS book
    Accessibility – to request an alternate format of an AMS title
Volume: 191995; 408 pp
MSC: Primary 62; 68; 92;

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.

Readership

This book is directed to a multidisciplinary audience who are interested in the problems associated with partitioning data.

  • Part I. Cluster Analysis Methods
  • The median procedure for partitions
  • Structural properties of pyramidal clustering
  • Partitioning by maximum adjacency search of graphs
  • From data to knowledge: Probabilist objects for a symbolic data analysis
  • A labeling algorithm for minimum sum of diameters partitioning of graphs
  • Agreement subtrees, metric and consensus for labeled binary trees
  • How to choose K entities among N
  • On the classification of monotone-equivariant cluster methods
  • Contiguity-constrained hierarchical clustering
  • Part II. Target Tracking
  • Image segmentation based on optimal layering for precision tracking
  • Multidimensional assignments and multitarget tracking
  • Part III. Computer Vision
  • Grouping edges: An efficient Bayesian multiple hypothesis approach
  • Finding salient convex groups
  • Mixture models for optical flow computation
  • Multilevel detection of stereo disparity surfaces
  • Part IV. Human Vision
  • Some problems of visual shape recognition to which the application of clustering mathematics might yield some potential benefits
  • Perceptual models of small dot clusters
  • Subjective contours in early vision and beyond
  • The visual perception of surfaces, their properties, and relationships
  • Visual computations and dot cluster
Review Copy – for reviewers who would like to review an AMS book
Accessibility – to request an alternate format of an AMS title
Please select which format for which you are requesting permissions.