DIMACS: Series in Discrete Mathematics and Theoretical Computer Science 1995; 408 pp; hardcover Volume: 19 ISBN10: 0821866060 ISBN13: 9780821866061 List Price: US$109 Member Price: US$87.20 Order Code: DIMACS/19
 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. Copublished with the Center for Discrete Mathematics and Theoretical Computer Science beginning with Volume 8. Volumes 17 were copublished with the Association for Computer Machinery (ACM). Readership This book is directed to a multidisciplinary audience who are interested in the problems associated with partitioning data. Table of Contents Part 1. Cluster Analysis Methods  J.P. Barthélemy and B. Leclerc  The median procedure for partitions
 P. Bertrand  Structural properties of pyramidal clustering
 W. Cai and D. W. Matula  Partitioning by maximum adjacency search of graphs
 E. Diday  From data to knowledge: Probabilist objects for a symbolic data analysis
 S. Gélinas, P. Hansen, and B. Jaumard  A labeling algorithm for minimum sum of diameters partitioning of graphs
 W. Goddard, E. Kubicka, G. Kubicki, and F. R. McMorris  Agreement subtrees, metric and consensus for labeled binary trees
 P. Hansen, B. Jaumard, and N. Mladenovic  How to choose \(K\) entities among N
 M. F. Janowitz and R. Wille  On the classification of monotoneequivariant cluster methods
 F. D. Murtagh  Contiguityconstrained hierarchical clustering
Part 2. Target Tracking  A. Kumar, Y. BarShalom, and E. Oron  Image segmentation based on optimal layering for precision tracking
 A. B. Poore  Multidimensional assignments and multitarget tracking
Part 3. Computer Vision  I. J. Cox, J. H. Rehg, S. L. Hingorani, and M. L. Miller  Grouping edges: An efficient Bayesian multiple hypothesis approach
 D. W. Jacobs  Finding salient convex groups
 A. Jepson and M. J. Black  Mixture models for optical flow computation
 Y. Yang and A. L. Yuille  Multilevel detection of stereo disparity surfaces
Part 4. Human Vision  I. Biederman  Some problems of visual shape recognition to which the application of clustering mathematics might yield some potential benefits
 J. Feldman  Perceptual models of small dot clusters
 B. Julesz  Subjective contours in early vision and beyond
 D. Kersten and S. Madarasmi  The visual perception of surfaces, their properties, and relationships
 S. W. Zucker  Visual computations and dot cluster
