A multi-objective approach to discover biclusters in microarray data
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Possibilistic approach for biclustering microarray data
Computers in Biology and Medicine
Unsupervised retrieval of attack profiles in collaborative recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
A Hybrid Metaheuristic for Biclustering Based on Scatter Search and Genetic Algorithms
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
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
Evolutionary metaheuristic for biclustering based on linear correlations among genes
Proceedings of the 2010 ACM Symposium on Applied Computing
Biclusters evaluation based on shifting and scaling patterns
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Negative correlations in collaboration: concepts and algorithms
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Comparative analysis of biclustering algorithms
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Iterated local search for biclustering of microarray data
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Measuring the quality of shifting and scaling patterns in biclusters
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Gene expression network discovery: a pattern based biclustering approach
Proceedings of the 2011 International Conference on Communication, Computing & Security
A novel probabilistic encoding for EAs applied to biclustering of microarray data
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A new framework for co-clustering of gene expression data
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
An effective measure for assessing the quality of biclusters
Computers in Biology and Medicine
Obtaining biclusters in microarrays with population-based heuristics
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Shifting patterns discovery in microarrays with evolutionary algorithms
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Correlation–based scatter search for discovering biclusters from gene expression data
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Data transformation for sum squared residue
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Finding gene coherent patterns using PATSUB+
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
BiMine+: An efficient algorithm for discovering relevant biclusters of DNA microarray data
Knowledge-Based Systems
A unified adaptive co-identification framework for high-d expression data
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
A Discrete Artificial Bees Colony Inspired Biclustering Algorithm
International Journal of Swarm Intelligence Research
CoBi: Pattern Based Co-Regulated Biclustering of Gene Expression Data
Pattern Recognition Letters
A new measure for gene expression biclustering based on non-parametric correlation
Computer Methods and Programs in Biomedicine
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Motivation: During the last years, the discovering of biclusters in data is becoming more and more popular. Biclustering aims at extracting a set of clusters, each of which might use a different subset of attributes. Therefore, it is clear that the usefulness of biclustering techniques is beyond the traditional clustering techniques, especially when datasets present high or very high dimensionality. Also, biclustering considers overlapping, which is an interesting aspect, algorithmically and from the point of view of the result interpretation. Since the Cheng and Church's works, the mean squared residue has turned into one of the most popular measures to search for biclusters, which ideally should discover shifting and scaling patterns. Results: In this work, we identify both types of patterns (shifting and scaling) and demonstrate that the mean squared residue is very useful to search for shifting patterns, but it is not appropriate to find scaling patterns because even when we find a perfect scaling pattern the mean squared residue is not zero. In addition, we provide an interesting result: the mean squared residue is highly dependent on the variance of the scaling factor, which makes possible that any algorithm based on this measure might not find these patterns in data when the variance of gene values is high. The main contribution of this paper is to prove that the mean squared residue is not precise enough from the mathematical point of view in order to discover shifting and scaling patterns at the same time. Contact: aguilar@lsi.us.es