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136,307
Distributional Clustering Of English Words
- In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics
, 1993
"... We describe and evaluate experimentally a method for clustering words according to their dis- tribution in particular syntactic contexts. Words are represented by the relative frequency distributions of contexts in which they appear, and relative entropy between those distributions is used as the si ..."
Abstract
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Cited by 631 (30 self)
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as the similarity measure for clustering. Clusters are represented by average context distributions derived from the given words according to their probabilities of cluster membership. In many cases, the clusters can be thought of as encoding coarse sense distinctions. Deterministic annealing is used to find lowest
A Highly-Scalable Cluster Membership Protocol
"... Membership management (knowledge of the running processors and fault-detection mechanism) is a crucial component of any cluster-based network service. To date, existing procotols scale poorly with the number of nodes, thus limiting the scale of cluster computing. This paper describes a new membe ..."
Abstract
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Membership management (knowledge of the running processors and fault-detection mechanism) is a crucial component of any cluster-based network service. To date, existing procotols scale poorly with the number of nodes, thus limiting the scale of cluster computing. This paper describes a new
X-RAYS AND YOUNG CLUSTERS Membership, IMFs and distances
, 2005
"... Sensitive imaging X-ray observations of young stellar clusters (YSCs, ages ≤ 10 Myr) are valuable tools for the acquisition of an unbiased census of cluster members needed for Initial Mass Function (IMF) studies. Several dozen YSCs, both nearby and across the Galactic disk, have been observed with t ..."
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Sensitive imaging X-ray observations of young stellar clusters (YSCs, ages ≤ 10 Myr) are valuable tools for the acquisition of an unbiased census of cluster members needed for Initial Mass Function (IMF) studies. Several dozen YSCs, both nearby and across the Galactic disk, have been observed
Mean shift, mode seeking, and clustering
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1995
"... Abstract-Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some k-means like clustering algorithms its special cases. It is shown that mean shift is a mode-seeki ..."
Abstract
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Cited by 620 (0 self)
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Abstract-Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some k-means like clustering algorithms its special cases. It is shown that mean shift is a mode
OPTICS: Ordering Points To Identify the Clustering Structure
, 1999
"... Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all of ..."
Abstract
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Cited by 511 (49 self)
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Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all
Model-Based Clustering, Discriminant Analysis, and Density Estimation
- JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2000
"... Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However, there is little ..."
Abstract
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Cited by 557 (28 self)
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Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However
Accepted to AJ A Probabilistic Quantification of Galaxy Cluster Membership
, 2000
"... Clusters of galaxies are important laboratories for understanding both galaxy evolution and constraining cosmological quantities. Any analysis of clusters, however, is best done when one can reliably determine which galaxies are members of the cluster. While this would ideally be done spectroscopica ..."
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, which, while useful in a global sense, does not provide information for specific galaxies. In this paper, we develop a more robust technique which uses photometrically estimated redshifts to determine cluster membership. This technique can either be used as an improvement over the commonly used
Results 1 - 10
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136,307
