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A PTAS for the minimum consensus clustering problem with a fixed number of clusters
 In Proc. 11th ICTCS
, 2009
"... The Consensus Clustering problem has been introduced as an effective way to analyze the results of different microarray experiments [5, 6]. The problem consists of looking for a partition that best summarizes a set of input partitions (each corresponding to a different microarray experiment) under a ..."
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Cited by 3 (0 self)
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The Consensus Clustering problem has been introduced as an effective way to analyze the results of different microarray experiments [5, 6]. The problem consists of looking for a partition that best summarizes a set of input partitions (each corresponding to a different microarray experiment) under
Laplacian eigenmaps and spectral techniques for embedding and clustering.
 Proceeding of Neural Information Processing Systems,
, 2001
"... Abstract Drawing on the correspondence between the graph Laplacian, the LaplaceBeltrami op erator on a manifold , and the connections to the heat equation , we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in ..."
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Cited by 667 (7 self)
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in a higher dimensional space. The algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality preserving properties and a natural connection to clustering. Several applications are considered. In many areas of artificial intelligence, information
Aggregating inconsistent information: ranking and clustering
, 2005
"... We address optimization problems in which we are given contradictory pieces of input information and the goal is to find a globally consistent solution that minimizes the number of disagreements with the respective inputs. Specifically, the problems we address are rank aggregation, the feedback arc ..."
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Cited by 225 (17 self)
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set problem on tournaments, and correlation and consensus clustering. We show that for all these problems (and various weighted versions of them), we can obtain improved approximation factors using essentially the same remarkably simple algorithm. Additionally, we almost settle a long
CLUSTERING
"... Abstract—We introduce a probabilistic version of the wellknown Rand Index (RI) for measuring the similarity between two partitions, called Probabilistic Rand Index (PRI), in which agreements and disagreements at the objectpair level are weighted according to the probability of their occurring by c ..."
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by chance. We then cast consensus clustering as an optimization problem of the PRI value between a target partition and a set of given partitions, experimenting with a simple and very efficient stochastic optimization algorithm. Remarkable performance gains over input partitions as well as over existing
A PTAS For The kConsensus Structures Problem Under Squared Euclidean Distance
, 2008
"... algorithms ..."
Clustering
, 2009
"... The problem is to construct an optimal weight tree from the 3 () n 4 weighted quartet topologies on n objects, where optimality means that the summed weight of the embedded quartet topologies is optimal (so it can be the case that the optimal tree embeds all quartets as nonoptimal topologies). We pr ..."
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Cited by 2 (0 self)
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The problem is to construct an optimal weight tree from the 3 () n 4 weighted quartet topologies on n objects, where optimality means that the summed weight of the embedded quartet topologies is optimal (so it can be the case that the optimal tree embeds all quartets as nonoptimal topologies). We
On the Parameterized Complexity of Consensus Clustering
, 2011
"... Given a collection C of partitions of a base set S, the NPhard Consensus Clustering problem asks for a partition of S which has a total Mirkin distance of at most t to the partitions in C, where t is a nonnegative integer. We present a parameterized algorithm for Consensus Clustering with running ..."
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Cited by 2 (2 self)
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Given a collection C of partitions of a base set S, the NPhard Consensus Clustering problem asks for a partition of S which has a total Mirkin distance of at most t to the partitions in C, where t is a nonnegative integer. We present a parameterized algorithm for Consensus Clustering
Consensus clustering + meta clustering = multiple consensus clustering
 Florida Artificial Intelligence Research Society Conference
, 2011
"... Consensus clustering and meta clustering are two important extensions of the classical clustering problem. Given a set of input clusterings of a given dataset, consensus clustering aims to find a single final clustering which is a better fit in some sense than the existing clusterings, and meta clus ..."
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Cited by 3 (0 self)
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Consensus clustering and meta clustering are two important extensions of the classical clustering problem. Given a set of input clusterings of a given dataset, consensus clustering aims to find a single final clustering which is a better fit in some sense than the existing clusterings, and meta
Results 1  10
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702,100