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708
A Memetic Heuristic for the Co-clustering Problem
"... Abstract. Co-clustering partitions two different kinds of objects simultaneously. Bregman co-clustering is a well-studied fast iterative algorithm to perform co-clustering. However, this method is very prone to local optima. We propose a memetic algorithm to solve the co-clustering problem. Experime ..."
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Abstract. Co-clustering partitions two different kinds of objects simultaneously. Bregman co-clustering is a well-studied fast iterative algorithm to perform co-clustering. However, this method is very prone to local optima. We propose a memetic algorithm to solve the co-clustering problem
Information-Theoretic Co-Clustering
- In KDD
, 2003
"... Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log and market-basket data analysis. A basic problem in contingency table analysis is co-clustering: simultaneous clustering of the rows and columns. A novel theoretical formulation views ..."
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Cited by 346 (12 self)
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Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log and market-basket data analysis. A basic problem in contingency table analysis is co-clustering: simultaneous clustering of the rows and columns. A novel theoretical formulation
Approximation algorithms for co-clustering
- In Proceedings PODS 2008
, 2008
"... Co-clustering is the simultaneous partitioning of the rows and columns of a matrix such that the blocks induced by the row/column partitions are good clusters. Motivated by several applications in text mining, market-basket analysis, and bioinformatics, this problem has attracted severe attention in ..."
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Cited by 19 (0 self)
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Co-clustering is the simultaneous partitioning of the rows and columns of a matrix such that the blocks induced by the row/column partitions are good clusters. Motivated by several applications in text mining, market-basket analysis, and bioinformatics, this problem has attracted severe attention
Modularity and Spectral Co-Clustering for Categorical Data
"... Abstract — To tackle the co-clustering problem on categorical data, we consider a spectral approach. We first define a generalized modularity measure for the co-clustering task. Then, we reformulate its maximization as a trace maximization problem. Finally we develop a spectral based co-clustering a ..."
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Abstract — To tackle the co-clustering problem on categorical data, we consider a spectral approach. We first define a generalized modularity measure for the co-clustering task. Then, we reformulate its maximization as a trace maximization problem. Finally we develop a spectral based co-clustering
Multiobjective Optimization of Co-Clustering Ensembles
"... Co-clustering is a machine learning task where the goal is to simultaneously develop clusters of the data and of their respective features. We address the use of co-clustering ensembles to establish a consensus co-clustering over the data. In this paper we develop a new preference-based multiobjecti ..."
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-based multiobjective optimization algorithm to compete with a previous gradient ascent approach in finding optimal co-clustering ensembles. Unlike the gradient ascent algorithm, our approach once tackles the co-clustering problem with multiple heuristics, then applies the gradient ascent algorithm’s joint heuristic
www.theoryofcomputing.org A Constant-Factor Approximation Algorithm for Co-clustering ∗
, 2011
"... Abstract: Co-clustering is the simultaneous partitioning of the rows and columns of a matrix such that the blocks induced by the row/column partitions are good clusters. Motivated by several applications in text mining, market-basket analysis, and bioinformatics, this problem has attracted a lot of ..."
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Abstract: Co-clustering is the simultaneous partitioning of the rows and columns of a matrix such that the blocks induced by the row/column partitions are good clusters. Motivated by several applications in text mining, market-basket analysis, and bioinformatics, this problem has attracted a lot
Co-clustering on manifolds
, 2009
"... Co-clustering is based on the duality between data points (e.g. documents) and features (e.g. words), i.e. data points can be grouped based on their distribution on features, while features can be grouped based on their distribution on the data points. In the past decade, several co-clustering algo- ..."
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Cited by 31 (5 self)
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-rithms have been proposed and shown to be superior to tra-ditional one-side clustering. However, existing co-clustering algorithms fail to consider the geometric structure in the data, which is essential for clustering data on manifold. To address this problem, in this paper, we propose a Dual Regularized Co-Clustering
Robust overlapping co-clustering
- Dept. of ECE, Univ. of Texas at Austin, IDEAL-TR09, Downloadable from http://www.lans.ece.utexas.edu/papers/ techreports/deodhar08ROCC.pdf
, 2008
"... Clustering problems often involve datasets where only a part of the data is relevant to the problem, e.g., in microarray data analysis only a subset of the genes show cohesive expressions within a subset of the conditions/features. On such datasets, in order to accurately identify meaningful cluster ..."
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Cited by 5 (4 self)
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that is restricted to traditional “one-sided” clustering. We propose Robust Overlapping Co-clustering (ROCC), a scalable and very versatile framework that addresses the problem of efficiently detecting dense, arbitrarily positioned, possibly overlapping co-clusters in a dataset. ROCC works with a large variety
Sleeved CoClustering
, 2004
"... A coCluster of a m × n matrix X is a submatrix determined by a subset of the rows and a subset of the columns. The problem of finding coClusters with specific properties is of interest, in particular, in the analysis of microarray experiments. In that case the entries of the matrix X are the express ..."
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Cited by 5 (0 self)
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A coCluster of a m × n matrix X is a submatrix determined by a subset of the rows and a subset of the columns. The problem of finding coClusters with specific properties is of interest, in particular, in the analysis of microarray experiments. In that case the entries of the matrix X
Evolutionary Spectral Co-Clustering
"... Abstract—Co-clustering is the problem of deriving submatrices from the larger data matrix by simultaneously clustering rows and columns of the data matrix. Traditional co-clustering techniques are inapplicable to problems where the relationship between the instances (rows) and features (columns) evo ..."
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Cited by 1 (0 self)
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Abstract—Co-clustering is the problem of deriving submatrices from the larger data matrix by simultaneously clustering rows and columns of the data matrix. Traditional co-clustering techniques are inapplicable to problems where the relationship between the instances (rows) and features (columns
Results 1 - 10
of
708
