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Cluster Computing

by Rajkumar Buyya, Hai Jin, Toni Cortes , 2002
"... this paper combines very good property for load balancing ..."
Abstract - Cited by 85 (1 self) - Add to MetaCart
this paper combines very good property for load balancing

GPFS: A Shared-Disk File System for Large Computing Clusters

by Frank Schmuck, Roger Haskin - In Proceedings of the 2002 Conference on File and Storage Technologies (FAST , 2002
"... GPFS is IBM's parallel, shared-disk file system for cluster computers, available on the RS/6000 SP parallel supercomputer and on Linux clusters. GPFS is used on many of the largest supercomputers in the world. GPFS was built on many of the ideas that were developed in the academic community ove ..."
Abstract - Cited by 518 (3 self) - Add to MetaCart
GPFS is IBM's parallel, shared-disk file system for cluster computers, available on the RS/6000 SP parallel supercomputer and on Linux clusters. GPFS is used on many of the largest supercomputers in the world. GPFS was built on many of the ideas that were developed in the academic community

On Spectral Clustering: Analysis and an algorithm

by Andrew Y. Ng, Michael I. Jordan, Yair Weiss - ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS , 2001
"... Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors in slightly ..."
Abstract - Cited by 1697 (13 self) - Add to MetaCart
in slightly different ways. Second, many of these algorithms have no proof that they will actually compute a reasonable clustering. In this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze

Spark: Cluster Computing with Working Sets

by Matei Zaharia, Mosharaf Chowdhury, Michael J. Franklin, Scott Shenker, Ion Stoica
"... MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable for other popular applications. This paper focuses on one such class o ..."
Abstract - Cited by 191 (8 self) - Add to MetaCart
MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable for other popular applications. This paper focuses on one such class

Cluster computing

by Baker Abdalhaq, Emilio Luque, B. Abdalhaq, T. Margalef, G. Bianchini, E. Luque, T. Margalef, G. Bianchini, E. Luque
"... Between classical and ideal: enhancing wildland fire prediction ..."
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Between classical and ideal: enhancing wildland fire prediction

Cluster Comput

by Kashif Bilal, Ahmad Fayyaz, K. Bilal (b, S. U. Khan, S. Usman
"... Power-aware resource allocation in computer clusters using dynamic threshold voltage scaling and dynamic voltage scaling: comparison and analysis ..."
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Power-aware resource allocation in computer clusters using dynamic threshold voltage scaling and dynamic voltage scaling: comparison and analysis

Survey of clustering algorithms

by Rui Xu, Donald Wunsch II - IEEE TRANSACTIONS ON NEURAL NETWORKS , 2005
"... Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the ..."
Abstract - Cited by 483 (4 self) - Add to MetaCart
, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts

Adaptive clustering for mobile wireless networks

by Chunhung Richard Lin, Mario Gerla - IEEE Journal on Selected Areas in Communications , 1997
"... This paper describes a self-organizing, multihop, mobile radio network, which relies on a code division access scheme for multimedia support. In the proposed network architecture, nodes are organized into nonoverlapping clusters. The clusters are independently controlled and are dynamically reconfig ..."
Abstract - Cited by 556 (11 self) - Add to MetaCart
This paper describes a self-organizing, multihop, mobile radio network, which relies on a code division access scheme for multimedia support. In the proposed network architecture, nodes are organized into nonoverlapping clusters. The clusters are independently controlled and are dynamically

Mean shift, mode seeking, and clustering

by Yizong Cheng - 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 - Cited by 620 (0 self) - Add to MetaCart
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

Knowledge acquisition via incremental conceptual clustering

by Douglas H. Fisher - Machine Learning , 1987
"... hill climbing Abstract. Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has ..."
Abstract - Cited by 754 (8 self) - Add to MetaCart
not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety
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