Results 1 
4 of
4
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)
 Add to MetaCart
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...
New Techniques for BestMatch Retrieval
 ACM Transactions on Information Systems
, 1990
"... A scheme to answer bestmatch queries from a file containing a collection of objects is described. A bestmatch query is to find the objects in the file that are closest (according to some (dis)similarity measure) to a given target. Previous work [5, 331 suggests that one can reduce the number of co ..."
Abstract

Cited by 58 (5 self)
 Add to MetaCart
A scheme to answer bestmatch queries from a file containing a collection of objects is described. A bestmatch query is to find the objects in the file that are closest (according to some (dis)similarity measure) to a given target. Previous work [5, 331 suggests that one can reduce the number of comparisons required to achieve the desired results using the triangle inequality, starting with a data structure for the file that reflects some precomputed intrafile distances. We generalize the technique to allow the optimum use of any given set of precomputed intrafile distances. Some empirical results are presented which illustrate the effectiveness of our scheme, and its performance relative to previous algorithms.
and Not Just Predicting Outcomes 1.1 Mathematical Analysis of Data
, 2008
"... The history of data analysis that is addressed here is underpinned by two themes, – those of tabular data analysis, and the analysis of collected heterogeneous data. “Exploratory data analysis ” is taken as the heuristic approach that begins with data and information and seeks underlying explanatio ..."
Abstract
 Add to MetaCart
(Show Context)
The history of data analysis that is addressed here is underpinned by two themes, – those of tabular data analysis, and the analysis of collected heterogeneous data. “Exploratory data analysis ” is taken as the heuristic approach that begins with data and information and seeks underlying explanation for what is observed or measured. I also cover some of the evolving context of research and applications, including scholarly publishing, technology transfer and the economic relationship of the university to society.
mation and Not Just Predicting Outcomes 1.1 Mathematical Analysis of Data
"... The history of data analysis that is addressed here is underpinned by two themes, – those of tabular data analysis, and the analysis of collected heterogeneous data. “Exploratory data analysis ” is taken as the heuristic approach that begins with data and information and seeks underlying explanation ..."
Abstract
 Add to MetaCart
(Show Context)
The history of data analysis that is addressed here is underpinned by two themes, – those of tabular data analysis, and the analysis of collected heterogeneous data. “Exploratory data analysis ” is taken as the heuristic approach that begins with data and information and seeks underlying explanation for what is observed or measured. I also cover some of the evolving context of research and applications, including scholarly publishing, technology transfer and the economic relationship of the university to society.