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A parallel cluster labeling . . .

by Mike Flanigan, Pablo Tamayo , 1992
"... We present an algorithm for cluster dynamics to efficiently simulate large systems on MIMD parallel computers with large numbers of processing nodes. The method divides physical space into rectangular cells which are assigned to processing nodes and combines a serial procedure, by which clusters are ..."
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We present an algorithm for cluster dynamics to efficiently simulate large systems on MIMD parallel computers with large numbers of processing nodes. The method divides physical space into rectangular cells which are assigned to processing nodes and combines a serial procedure, by which clusters

Cone cluster labeling for support vector clustering

by Sei-hyung Lee - In Proceedings of 6th SIAM Conference on Data Mining , 2006
"... Clustering forms natural groupings of data points that maximize intra-cluster similarity and minimize intercluster similarity. Support Vector Clustering (SVC) is a clustering algorithm that can handle arbitrary cluster shapes. One of the major SVC challenges is a cluster labeling performance bottlen ..."
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Clustering forms natural groupings of data points that maximize intra-cluster similarity and minimize intercluster similarity. Support Vector Clustering (SVC) is a clustering algorithm that can handle arbitrary cluster shapes. One of the major SVC challenges is a cluster labeling performance

Abstract Cone Cluster Labeling for Support Vector Clustering

by Sei-hyung Lee
"... Clustering forms natural groupings of data points that maximize intra-cluster similarity and minimize intercluster similarity. Support Vector Clustering (SVC) is a clustering algorithm that can handle arbitrary cluster shapes. One of the major SVC challenges is a cluster labeling performance bottlen ..."
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Clustering forms natural groupings of data points that maximize intra-cluster similarity and minimize intercluster similarity. Support Vector Clustering (SVC) is a clustering algorithm that can handle arbitrary cluster shapes. One of the major SVC challenges is a cluster labeling performance

CLUSTER LABELING WITH LINKED DATA 1

by Martin Dostal, Michal Nykl, Karel Ježek
"... In this article, we would like to introduce our approach to cluster labeling with Linked Data. Clustering web pages into semantically related groups promises better performance in searching the Web. Nowadays, only special semantic search engines provide clustering of results. Other engines are doubt ..."
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In this article, we would like to introduce our approach to cluster labeling with Linked Data. Clustering web pages into semantically related groups promises better performance in searching the Web. Nowadays, only special semantic search engines provide clustering of results. Other engines

Comprehensible and Accurate Cluster Labels in Text Clustering

by Jerzy Stefanowski, Dawid Weiss
"... The purpose of text clustering in information retrieval is to discover groups of semantically related documents. Accurate and comprehensible cluster descriptions (labels) let the user comprehend the collection’s content faster and are essential for various document browsing interfaces. The task of c ..."
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The purpose of text clustering in information retrieval is to discover groups of semantically related documents. Accurate and comprehensible cluster descriptions (labels) let the user comprehend the collection’s content faster and are essential for various document browsing interfaces. The task

Analysis of Structural Relationships for Hierarchical Cluster Labeling

by Markus Muhr , Roman Kern , Michael Granitzer - In SIGIR , 2010
"... ABSTRACT Cluster label quality is crucial for browsing topic hierarchies obtained via document clustering. Intuitively, the hierarchical structure should influence the labeling accuracy. However, most labeling algorithms ignore such structural properties and therefore, the impact of hierarchical st ..."
Abstract - Cited by 5 (0 self) - Add to MetaCart
ABSTRACT Cluster label quality is crucial for browsing topic hierarchies obtained via document clustering. Intuitively, the hierarchical structure should influence the labeling accuracy. However, most labeling algorithms ignore such structural properties and therefore, the impact of hierarchical

Automatic Word Sense Discrimination

by Hinrich Schütze - Journal of Computational Linguistics , 1998
"... This paper presents context-group discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a high-dimensional, real-valued space in which closen ..."
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This paper presents context-group discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a high-dimensional, real-valued space in which

Parallel Cluster Labeling on a Network of Workstations

by Felipe Knop, Vernon Rego - In Proceedings of the Thirteenth Brazilian Symposium on Computer Networks , 1995
"... In recent years, encouraged by today's fast workstations and by software systems designed to transform workstation clusters into parallel programming environments, network of workstations have been increasingly used as computational engines. Networked workstations, however, are not ideal replac ..."
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applications (that generally do not require large amounts of communication), EcliPSe can be used for more general forms of message-passing parallel processing. We describe the use of the toolkit in the parallelization of a cluster labeling algorithm. The algorithm is designed so that it uses some of Ecli

Global and Local Information in Clustering Labeled Block Models

by Varun Kanade, Elchanan Mossel, Tselil Schramm , 2014
"... The stochastic block model is a classical cluster-exhibiting random graph model that has been widely studied in statistics, physics and computer science. In its simplest form, the model is a random graph with two equal-sized clusters, with intra-cluster edge probability p, and inter-cluster edge pro ..."
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in clustering by studying a labeled block model, where in addition to the edge information, the true cluster labels of a small fraction of the nodes are revealed. In the case of two clusters, we show that below the threshold, a small amount of node information does not affect recovery. On the other hand, we

New SIMD Algorithms for Cluster Labeling on Parallel Computers

by John Apostolakis, Paul Coddington, Enzo Marinari , 1992
"... Cluster algorithms are non-local Monte Carlo update schemes which can greatly increase the efficiency of computer simulations of spin models of magnets. The major computational task in these algorithms is connected component labeling, to identify clusters of connected sites on a lattice. We have dev ..."
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Cluster algorithms are non-local Monte Carlo update schemes which can greatly increase the efficiency of computer simulations of spin models of magnets. The major computational task in these algorithms is connected component labeling, to identify clusters of connected sites on a lattice. We have
Results 1 - 10 of 3,320