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Parallel Algorithms for Hierarchical Clustering
 Parallel Computing
, 1995
"... Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n 2 ) algorithms are known for this problem [3, 4, 10, 18]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms f ..."
Abstract

Cited by 107 (2 self)
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Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n 2 ) algorithms are known for this problem [3, 4, 10, 18]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms for hierarchical clustering. Parallel algorithms to perform hierarchical clustering using several distance metrics are then described. Optimal PRAM algorithms using n log n processors are given for the average link, complete link, centroid, median, and minimum variance metrics. Optimal butterfly and tree algorithms using n log n processors are given for the centroid, median, and minimum variance metrics. Optimal asymptotic speedups are achieved for the best practical algorithm to perform clustering using the single link metric on a n log n processor PRAM, butterfly, or tree. Keywords. Hierarchical clustering, pattern analysis, parallel algorithm, butterfly network, PRAM algorithm. 1 In...
Solution of the Simultaneous Pose and Correspondence Problem Using Gaussian Error Model
 COMPUTER VISION AND IMAGE UNDERSTANDING
, 1999
"... ..."
Image Understanding Research at Colorado State University
, 1997
"... Colorado State University is initiating a new project on learning control strategies for object recognition. It is our belief that the current library of IU algorithms is sufficient for solving many practical tasks if we can only learn to sequence them properly. We are investigating the use of open ..."
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Colorado State University is initiating a new project on learning control strategies for object recognition. It is our belief that the current library of IU algorithms is sufficient for solving many practical tasks if we can only learn to sequence them properly. We are investigating the use of openloop and closedloop control policies for sequencing IU algorithms, emphasizing the use of Markov decision models and reinforcement learning to derive closedloop object recognition policies. This work is being conducted in the context of the Automatic Population of Geospatial Databases (APGD) project, where it will be used to learn object recognition strategies for finding buildings, roads and other objects of interest in aerial images.