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136
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 226 (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
Weighted consensus clustering
, 2008
"... Consensus clustering has emerged as an important extension of the classical clustering problem. We propose weighted consensus clustering, where each input clustering is weighted and the weights are determined in such a way that the final consensus clustering provides a better quality solution, in wh ..."
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Cited by 29 (10 self)
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which may resolve the difficult situation when the input clusterings diverge significantly. We also show that the weighted consensus clustering resolves the redundancy problem when many input clusterings correlate highly. Detailed algorithms are given. Experiments are carried out to demonstrate
A Polynomial Time Approximation Scheme for kConsensus Clustering
, 2010
"... This paper introduces a polynomial time approximation scheme for the metric Correlation Clustering problem, when the number of clusters returned is bounded (by k). Consensus Clustering is a fundamental aggregation problem, with considerable application, and it is analysed here as a metric variant of ..."
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Cited by 2 (0 self)
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This paper introduces a polynomial time approximation scheme for the metric Correlation Clustering problem, when the number of clusters returned is bounded (by k). Consensus Clustering is a fundamental aggregation problem, with considerable application, and it is analysed here as a metric variant
On Constructing An Optimal Consensus Clustering from Multiple Clusterings
, 2007
"... Computing a suitable measure of consensus among several clusterings on the same data is an important problem that arises in several areas such as computational biology and data mining. In this paper, we formalize a settheoretic model for computing such a similarity measure. Roughly speaking, in thi ..."
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Cited by 5 (2 self)
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Computing a suitable measure of consensus among several clusterings on the same data is an important problem that arises in several areas such as computational biology and data mining. In this paper, we formalize a settheoretic model for computing such a similarity measure. Roughly speaking
On aggregate control of clustered consensus networks
 in American Control Conference (ACC), 2015. IEEE
"... Abstract — We address a consensus control problem for networks that have multiple dense areas with sparse interconnection structure. The sparsity pattern in such networks naturally gives rise to a timescale separation in its dynamics, whereby nodes inside an area synchronize over a fast timescal ..."
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Cited by 1 (0 self)
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show that when these individual controllers are implemented on the actual network model, the closedloop response is close to that obtained from the approximate models, provided that the clustering is strong. The design procedure is demonstrated by a simulation example. Index Terms — Large
Abstract Consensus Clustering Algorithms: Comparison and Refinement
"... Consensus clustering is the problem of reconciling clustering information about the same data set coming from different sources or from different runs of the same algorithm. Cast as an optimization problem, consensus clustering is known as median partition, and has been shown to be NPcomplete. A nu ..."
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Cited by 24 (0 self)
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Consensus clustering is the problem of reconciling clustering information about the same data set coming from different sources or from different runs of the same algorithm. Cast as an optimization problem, consensus clustering is known as median partition, and has been shown to be NPcomplete. A
Approximation algorithms for biclustering problems
 In Proc. 6th WABI, volume 4175 of LNBI
, 2006
"... Abstract. One of the main goals in the analysis of microarray data is to identify groups of genes and groups of experimental conditions (including environments, individuals, and tissues) that exhibit similar expression patterns. This is the socalled biclustering problem. In this paper, we consider ..."
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Cited by 1 (0 self)
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two variations of the biclustering problem: the consensus submatrix problem and the bottleneck submatrix problem. The input of the problems contains an m × n matrix A and integers l and k. The consensus submatrix problem is to find an l × k submatrix with l<mand k<nand a consensus vector
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
Deterministic pivoting algorithms for constrained ranking and Clustering Problems
, 2007
"... We consider ranking and clustering problems related to the aggregation of inconsistent information, in particular, rank aggregation, (weighted) feedback arc set in tournaments, consensus and correlation clustering, and hierarchical clustering. Ailon, Charikar, and Newman [4], Ailon and Charikar [3], ..."
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Cited by 34 (4 self)
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We consider ranking and clustering problems related to the aggregation of inconsistent information, in particular, rank aggregation, (weighted) feedback arc set in tournaments, consensus and correlation clustering, and hierarchical clustering. Ailon, Charikar, and Newman [4], Ailon and Charikar [3
Results 1  10
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136