Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
245,240
Hierarchical Clustering
"... Given n points in d-dimensional space, the goal of hierarchical clustering is to create asequenceofnestedpartitions,whichcanbeconvenientlyvisualized via a tree or hierarchy of clusters, also called the cluster dendogram. Theclustersinthehierarchy range from the fine-grained to the coarse-grained – t ..."
Abstract
- Add to MetaCart
Given n points in d-dimensional space, the goal of hierarchical clustering is to create asequenceofnestedpartitions,whichcanbeconvenientlyvisualized via a tree or hierarchy of clusters, also called the cluster dendogram. Theclustersinthehierarchy range from the fine-grained to the coarse
Hierarchical Clustering
"... Given n points in d-dimensional space, the goal of hierarchical clustering is to create asequenceofnestedpartitions,whichcanbeconvenientlyvisualized via a tree or hierarchy of clusters, also called the cluster dendogram. Theclustersinthehierarchy range from the fine-grained to the coarse-grained – t ..."
Abstract
- Add to MetaCart
Given n points in d-dimensional space, the goal of hierarchical clustering is to create asequenceofnestedpartitions,whichcanbeconvenientlyvisualized via a tree or hierarchy of clusters, also called the cluster dendogram. Theclustersinthehierarchy range from the fine-grained to the coarse
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
Hierarchical Clustering
, 2002
"... In this contribution I present current results on how galaxies, groups, clusters and superclusters cluster at low (z1) redshifts. I also discuss the measured and expected clustering evolution. In a program to study the clustering properties of small galaxy structures we have identified close pair ..."
Abstract
- Add to MetaCart
In this contribution I present current results on how galaxies, groups, clusters and superclusters cluster at low (z1) redshifts. I also discuss the measured and expected clustering evolution. In a program to study the clustering properties of small galaxy structures we have identified close
hierarchical clustering†
, 2004
"... A structure–odour relationship study using EVA descriptors and ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
A structure–odour relationship study using EVA descriptors and
Hierarchical Clustering
"... Abstract — Clustering techniques have a wide use and importance nowadays. This importance tends to increase as the amount of data grows and the processing power of the computers increases. Clustering applications are used extensively in various fields such as artificial intelligence, pattern recogni ..."
Abstract
- Add to MetaCart
Abstract — Clustering techniques have a wide use and importance nowadays. This importance tends to increase as the amount of data grows and the processing power of the computers increases. Clustering applications are used extensively in various fields such as artificial intelligence, pattern
Evaluation of Hierarchical Clustering Algorithms for Document Datasets
- Data Mining and Knowledge Discovery
, 2002
"... Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, hierarchical clustering solutions provide a view of the data at ..."
Abstract
-
Cited by 252 (6 self)
- Add to MetaCart
Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, hierarchical clustering solutions provide a view of the data
Bayesian hierarchical clustering
- In Proceedings of the 22nd International Conference on Machine Learning. ACM
, 2005
"... We present a novel algorithm for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. This algorithm has several advantages over traditional distance-based agglomerative clustering algorithms. (1) It defines a probabilistic model of the data which ..."
Abstract
-
Cited by 69 (11 self)
- Add to MetaCart
We present a novel algorithm for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. This algorithm has several advantages over traditional distance-based agglomerative clustering algorithms. (1) It defines a probabilistic model of the data which
Results 1 - 10
of
245,240
