Partitioning Data Sets

Partitioning Data Sets

by Ingemar J. Cox
     
 

ISBN-10: 0821866060

ISBN-13: 9780821866061

Pub. Date: 04/07/1995

Publisher: American Mathematical Society

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

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Overview

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.

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Product Details

ISBN-13:
9780821866061
Publisher:
American Mathematical Society
Publication date:
04/07/1995
Series:
DIMACS: Series in Discrete Mathematics and Theoretical Computer Science Ser., #19
Pages:
408

Table of Contents

Foreword
Preface
The Median Procedure for Partitions3
Structural Properties of Pyramidal Clustering35
Partitioning by Maximum Adjacency Search of Graphs55
From Data to Knowledge: Probabilist Objects for a Symbolic Data Analysis65
A Labeling Algorithm for Minimum Sum of Diameters Partitioning of Graphs89
Agreement Subtrees, Metric and Consensus for Labeled Binary Trees97
How to Choose K Entities Among N105
On the Classification of Monotone-Equivariant Cluster Methods117
Contiguity-Constrained Hierarchical Clustering143
Image Segmentation Based on Optimal Layering for Precision Tracking155
Multidimensional Assignments and Multitarget Tracking169
Grouping Edges: An Efficient Bayesian Multiple Hypothesis Approach199
Finding Salient Convex Groups237
Mixture Models for Optical Flow Computation271
Multilevel Detection of Stereo Disparity Surfaces287
Some Problems of Visual Shape Recognition to which the Application of Clustering Mathematics Might Yield Some Potential Benefits313
Perceptual Models of Small Dot Clusters331
Subjective Contours in Early Vision and Beyond359
The Visual Perception of Surfaces, their Properties, and Relationships373
Visual Computations and Dot Cluster391

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