Partitioning Data Sets

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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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Editorial Reviews

Refereed versions of papers from a workshop held at DIMACS at Rutgers U. in April 1993, include essays and surveys on cluster analysis theory, methods and applications and related problems in psychology, computer vision, and target tracking. Among the topics are the median procedure for partitions; image segmentation based on optimal layering for precision tracking; mixture models for optical flow computation; and the visual perception of surfaces, their properties, and relationships. No index. Annotation c. Book News, Inc., Portland, OR (
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Product Details

Table of Contents

The Median Procedure for Partitions 3
Structural Properties of Pyramidal Clustering 35
Partitioning by Maximum Adjacency Search of Graphs 55
From Data to Knowledge: Probabilist Objects for a Symbolic Data Analysis 65
A Labeling Algorithm for Minimum Sum of Diameters Partitioning of Graphs 89
Agreement Subtrees, Metric and Consensus for Labeled Binary Trees 97
How to Choose K Entities Among N 105
On the Classification of Monotone-Equivariant Cluster Methods 117
Contiguity-Constrained Hierarchical Clustering 143
Image Segmentation Based on Optimal Layering for Precision Tracking 155
Multidimensional Assignments and Multitarget Tracking 169
Grouping Edges: An Efficient Bayesian Multiple Hypothesis Approach 199
Finding Salient Convex Groups 237
Mixture Models for Optical Flow Computation 271
Multilevel Detection of Stereo Disparity Surfaces 287
Some Problems of Visual Shape Recognition to which the Application of Clustering Mathematics Might Yield Some Potential Benefits 313
Perceptual Models of Small Dot Clusters 331
Subjective Contours in Early Vision and Beyond 359
The Visual Perception of Surfaces, their Properties, and Relationships 373
Visual Computations and Dot Cluster 391
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