Partitioning Data Sets ( DIMACS - Series in Discrete Mathematics and Theoretical Computer Science )

Publication series ：DIMACS - Series in Discrete Mathematics and Theoretical Computer Science

Author： Ingemar J. Cox;Pierre Hansen;Bela Julesz  

Publisher： American Mathematical Society‎

Publication year： 2017

E-ISBN: 9781470439774

P-ISBN(Paperback): 9780821866061

Subject： O1 Mathematics

Keyword： 暂无分类

Language： ENG

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Partitioning Data Sets

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.

Chapter

Cover

Title page

Contents

Foreword

Preface

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

Back Cover

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