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99,322
Consistency of spectral clustering
, 2004
"... Consistency is a key property of statistical algorithms, when the data is drawn from some underlying probability distribution. Surprisingly, despite decades of work, little is known about consistency of most clustering algorithms. In this paper we investigate consistency of a popular family of spe ..."
Abstract

Cited by 572 (15 self)
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Consistency is a key property of statistical algorithms, when the data is drawn from some underlying probability distribution. Surprisingly, despite decades of work, little is known about consistency of most clustering algorithms. In this paper we investigate consistency of a popular family
Learning with local and global consistency.
 In NIPS,
, 2003
"... Abstract We consider the general problem of learning from labeled and unlabeled data, which is often called semisupervised learning or transductive inference. A principled approach to semisupervised learning is to design a classifying function which is sufficiently smooth with respect to the intr ..."
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Cited by 673 (21 self)
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Abstract We consider the general problem of learning from labeled and unlabeled data, which is often called semisupervised learning or transductive inference. A principled approach to semisupervised learning is to design a classifying function which is sufficiently smooth with respect
Globally Consistent Range Scan Alignment for Environment Mapping
 AUTONOMOUS ROBOTS
, 1997
"... A robot exploring an unknown environmentmay need to build a world model from sensor measurements. In order to integrate all the frames of sensor data, it is essential to align the data properly. An incremental approach has been typically used in the past, in which each local frame of data is alig ..."
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Cited by 531 (8 self)
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A robot exploring an unknown environmentmay need to build a world model from sensor measurements. In order to integrate all the frames of sensor data, it is essential to align the data properly. An incremental approach has been typically used in the past, in which each local frame of data
Consistent Data Retrieval
, 1994
"... When dealing with inconsistent databases the usual approach is to repair them, i.e. take them back to a consistent state to be able to retrieve meaningful (consistent) data. This, however, has one serious drawback: it generally involves discarding inconsistent data, hence losing potentially useful ..."
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Cited by 1 (0 self)
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When dealing with inconsistent databases the usual approach is to repair them, i.e. take them back to a consistent state to be able to retrieve meaningful (consistent) data. This, however, has one serious drawback: it generally involves discarding inconsistent data, hence losing potentially
Weighted Voting for Replicated Data
, 1979
"... In a new algorithm for maintaining replicated data, every copy of a replicated file is assigned some number of votes. Every transaction collects a read quorum of r votes to read a file, and a write quorum of w votes to write a file, such that r+w is greater than the total number number of votes assi ..."
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Cited by 598 (0 self)
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In a new algorithm for maintaining replicated data, every copy of a replicated file is assigned some number of votes. Every transaction collects a read quorum of r votes to read a file, and a write quorum of w votes to write a file, such that r+w is greater than the total number number of votes
Implementing data cubes efficiently
 In SIGMOD
, 1996
"... Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like total ..."
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Cited by 548 (1 self)
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Decision support applications involve complex queries on very large databases. Since response times should be small, query optimization is critical. Users typically view the data as multidimensional data cubes. Each cell of the data cube is a view consisting of an aggregation of interest, like
Powerlaw distributions in empirical data
 ISSN 00361445. doi: 10.1137/ 070710111. URL http://dx.doi.org/10.1137/070710111
, 2009
"... Powerlaw distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and manmade phenomena. Unfortunately, the empirical detection and characterization of power laws is made difficult by the large fluctuations that occur in the t ..."
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Cited by 607 (7 self)
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demonstrate these methods by applying them to twentyfour realworld data sets from a range of different disciplines. Each of the data sets has been conjectured previously to follow a powerlaw distribution. In some cases we find these conjectures to be consistent with the data while in others the power law
Longitudinal data analysis using generalized linear models”.
 Biometrika,
, 1986
"... SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating ..."
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Cited by 1526 (8 self)
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SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence
Eraser: a dynamic data race detector for multithreaded programs
 ACM Transaction of Computer System
, 1997
"... Multithreaded programming is difficult and error prone. It is easy to make a mistake in synchronization that produces a data race, yet it can be extremely hard to locate this mistake during debugging. This paper describes a new tool, called Eraser, for dynamically detecting data races in lockbased ..."
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Cited by 688 (2 self)
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Multithreaded programming is difficult and error prone. It is easy to make a mistake in synchronization that produces a data race, yet it can be extremely hard to locate this mistake during debugging. This paper describes a new tool, called Eraser, for dynamically detecting data races in lock
BIRCH: an efficient data clustering method for very large databases
 In Proc. of the ACM SIGMOD Intl. Conference on Management of Data (SIGMOD
, 1996
"... Finding useful patterns in large datasets has attracted considerable interest recently, and one of the most widely st,udied problems in this area is the identification of clusters, or deusel y populated regions, in a multidir nensional clataset. Prior work does not adequately address the problem of ..."
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Cited by 576 (2 self)
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of large datasets and minimization of 1/0 costs. This paper presents a data clustering method named Bfll (;”H (Balanced Iterative Reducing and Clustering using Hierarchies), and demonstrates that it is especially suitable for very large databases. BIRCH incrementally and clynamicall y clusters incoming
Results 1  10
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99,322