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Network models of massive datasets
 Computer Science and Information Systems
"... Abstract. We give a brief overview of the methodology of modeling massive datasets arising in various applications as networks. This approach is often useful for extracting nontrivial information from the datasets by applying standard graphtheoretic techniques. We also point out that graphs repres ..."
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Cited by 3 (1 self)
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Abstract. We give a brief overview of the methodology of modeling massive datasets arising in various applications as networks. This approach is often useful for extracting nontrivial information from the datasets by applying standard graphtheoretic techniques. We also point out that graphs
Spatial Prediction for Massive Datasets
"... Remotely sensed spatiotemporal datasets on the order of megabytes to terrabytes are becoming more common. For example, polarorbiting satellites observe Earth from space, monitoring the Earth’s atmospheric, oceanic, and terrestrial processes, and generate massive amounts of environmental data. The ..."
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Cited by 8 (5 self)
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Remotely sensed spatiotemporal datasets on the order of megabytes to terrabytes are becoming more common. For example, polarorbiting satellites observe Earth from space, monitoring the Earth’s atmospheric, oceanic, and terrestrial processes, and generate massive amounts of environmental data
Clustering Massive Datasets
, 1998
"... Clustering data is not an easy problem in general, and is compounded for a massive dataset. Restricting attention to a sample from the data ignores minority groups and hence compromises on the available riches. This paper develops, under Gaussian assumptions, a multistage sequential algorithm. After ..."
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Clustering data is not an easy problem in general, and is compounded for a massive dataset. Restricting attention to a sample from the data ignores minority groups and hence compromises on the available riches. This paper develops, under Gaussian assumptions, a multistage sequential algorithm
Optimization Methods In Massive Datasets
"... We describe the role of generalized support vector machines in separating massive and complex data using arbitrary nonlinear kernels. Feature selection that improves generalization is implemented via an effective procedure that utilizes a polyhedral norm or a concave function minimization. Massive d ..."
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Cited by 8 (1 self)
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We describe the role of generalized support vector machines in separating massive and complex data using arbitrary nonlinear kernels. Feature selection that improves generalization is implemented via an effective procedure that utilizes a polyhedral norm or a concave function minimization. Massive
Error Correction for Massive Datasets
 Optimization Methods and Software
, 2005
"... The paper is concerned with the problem of automatic detection and correction of errors into massive data sets. As customary, erroneous data records are detected by formulating a set of rules. Such rules are here encoded into linear inequalities. This allows to check the set of rules for inconsisten ..."
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Cited by 5 (2 self)
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The paper is concerned with the problem of automatic detection and correction of errors into massive data sets. As customary, erroneous data records are detected by formulating a set of rules. Such rules are here encoded into linear inequalities. This allows to check the set of rules
Clustering Massive Datasets
, 2005
"... This Thesis is brought to you for free and open access by the Graduate School at Trace: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Masters Theses by an authorized administrator of Trace: Tennessee Research and Creative Exchange. For more information, ..."
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This Thesis is brought to you for free and open access by the Graduate School at Trace: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Masters Theses by an authorized administrator of Trace: Tennessee Research and Creative Exchange. For more information,
Massive Datasets In Astronomy
"... Astronomy has a long history of acquiring, systematizing, and interpreting large quantities of data. Starting from the earliest sky atlases through the first major photographic sky surveys of the 20th century, this tradition is continuing today, and at an ever increasing rate. ..."
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Astronomy has a long history of acquiring, systematizing, and interpreting large quantities of data. Starting from the earliest sky atlases through the first major photographic sky surveys of the 20th century, this tradition is continuing today, and at an ever increasing rate.
Chapter 1 MASSIVE DATASETS IN ASTRONOMY
, 2001
"... Astronomy has a long history of acquiring, systematizing, and interpreting large quantities of data. Starting from the earliest sky atlases through the first major photographic sky surveys of the 20th century, this tradition is continuing today, and at an ever increasing rate. Like many other fields ..."
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of the major datasets in astronomy, discuss different techniques used for archiving data, and conclude with a discussion of the future of massive datasets in astronomy. Keywords:
Structural Inferences from Massive Datasets
 in Proceedings of IJCAI
, 1997
"... Highlevel understanding of data must involve the interplay between substantial prior knowledge with geometric and statistical techniques. Our approach emphasizes the recovery of basic structural elements and their interaction patterns in order to summarize and draw inferences about the significant ..."
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Cited by 10 (0 self)
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features contained in the data. As a testbed for modeling how scientists analyze and extract knowledge of structure morphogenesis from data, we examine the datasets obtained from numerical simulation of turbulence. We describe a program that automatically extracts 3D structures, classifies them
Learning Bayesian network structure from massive datasets: the “sparse candidate” algorithm
 In Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI
, 1999
"... Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing learning tools address this optimization problem using standard heuristic search techniques. Since the sear ..."
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Cited by 247 (7 self)
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Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing learning tools address this optimization problem using standard heuristic search techniques. Since the search space is extremely large, such search procedures can spend most of the time examining candidates that are extremely unreasonable. This problem becomes critical when we deal with data sets that are large either in the number of instances, or the number of attributes. In this paper, we introduce an algorithm that achieves faster learning by restricting the search space. This iterative algorithm restricts the parents of each variable to belong to a small subset of candidates. We then search for a network that satisfies these constraints. The learned network is then used for selecting better candidates for the next iteration. We evaluate this algorithm both on synthetic and reallife data. Our results show that it is significantly faster than alternative search procedures without loss of quality in the learned structures. 1
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