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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
ReadershipTable 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