AMS eBook CollectionsOne of the world's most respected mathematical collections, available in digital format for your library or institution
Partitioning Data Sets
About this Title
Ingemar Cox, NEC Research Institute, Princeton, NJ, Pierre Hansen, GERARD, Montreal, PQ, Canada and Bela Julesz, Rutgers University, New Brunswick, NJ, Editors
Publication: DIMACS Series in Discrete Mathematics and Theoretical Computer Science
Publication Year:
1995; Volume 19
ISBNs: 978-0-8218-6606-1 (print); 978-1-4704-3977-4 (online)
DOI: https://doi.org/10.1090/dimacs/019
MathSciNet review: MR1326608
MSC: Primary 00B25
Table of Contents
Front/Back Matter
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