Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
Mining Shifting-and-Scaling Co-Regulation Patterns on Gene Expression Profiles
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Shifting and scaling patterns from gene expression data
Bioinformatics
Possibilistic approach for biclustering microarray data
Computers in Biology and Medicine
Virtual error: a new measure for evolutionary biclustering
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Application of Simulated Annealing to the Biclustering of Gene Expression Data
IEEE Transactions on Information Technology in Biomedicine
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The most widespread biclustering algorithms use the Mean Squared Residue (MSR) as measure for assessing the quality of biclusters. MSR can identify correctly shifting patterns, but fails at discovering biclusters presenting scaling patterns. Virtual Error (VE) is a measure which improves the performance of MSR in this sense, since it is effective at recognizing biclusters containing shifting patters or scaling patterns as quality biclusters. However, VE presents some drawbacks when the biclusters present both kind of patterns simultaneously. In this paper, we propose a improvement of VE that can be integrated in any heuristic to discover biclusters with shifting and scaling patterns simultaneously.