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Synchronized Clustering: A Review On Systematic Comparisons And Validation Of Prominent Block-Clustering Algorithms
2017
Zenodo
The main goal of this paper is to provide a systematic comparison and validation of prominent bi-clustering algorithms. ...
Abstract: One of the major problems in clustering is the need of specifying the optimal number of clusters in some clustering algorithms. ...
Fig. 3 . a) Effect of overlapping in the algorithm and the biclusters. 1) Best SS measure achieved by using F1 statistics along with the mean of SS for all the proven configurations. 2) Variation in the ...
doi:10.5281/zenodo.821158
fatcat:ys4cxx6cyfbxjdl4phif452kfi
Biclustering Models for Structured Microarray Data
2005
IEEE/ACM Transactions on Computational Biology & Bioinformatics
In response to this increase in data complexity, we propose some extensions to the plaid model, a biclustering method developed for the analysis of gene expression data. ...
We describe how the extended models mav be fitted and illustrate their use on real data. ...
The algorithm finds a bicluster of genes and samples for which the layer model fits better than the null model. ...
doi:10.1109/tcbb.2005.49
pmid:17044169
fatcat:sawbgjf52nhalnmhzvm626wfte
A New Semi-supervised Clustering Algorithm Based on Variational Bayesian and Its Application
2016
TELKOMNIKA (Telecommunication Computing Electronics and Control)
In order to further improve the performance of biclustering algorithm, this paper proposes a semisupervised clustering algorithm based on variational Bayesian. ...
Firstly, it introduces supplementary information of row and column for biclustering process and represents corresponding joint distribution probability model. ...
and corresponds to a lower bound for the Bayesian evidence, is a key quantity for model selection. ...
doi:10.12928/telkomnika.v14i3.3805
fatcat:nbg252n7mbey5lvlunhezq2o2m
Configurable pattern-based evolutionary biclustering of gene expression data
2013
Algorithms for Molecular Biology
Biclustering algorithms for microarray data aim at discovering functionally related gene sets under different subsets of experimental conditions. ...
Results: Here, we present the first biclustering algorithm in which it is possible to particularize several biclusters features in terms of different objectives. ...
The algorithm takes as input paramater the number of partial models passed for each iteration . ...
doi:10.1186/1748-7188-8-4
pmid:23433178
pmcid:PMC3668234
fatcat:mxgjhqyisbby3b5usbgx5qsgaq
Finding large average submatrices in high dimensional data
2009
Annals of Applied Statistics
Biclustering methods search for sample-variable associations in the form of distinguished submatrices of the data matrix. (The rows and columns of a submatrix need not be contiguous.) ...
The search for sample-variable associations is an important problem in the exploratory analysis of high dimensional data. ...
Biclusters identified by the CC algorithm have the smallest average variance, as CC searches for biclusters with low unexplained variation. ...
doi:10.1214/09-aoas239
fatcat:mnznvgq3ozdx7kf65ascv7yidi
FABIA: factor analysis for bicluster acquisition
2010
Computer applications in the biosciences : CABIOS
On these data sets, FABIA was able to separate spurious biclusters from true biclusters by ranking biclusters according to their information content. ...
Results: On 100 simulated data sets with known true, artificially implanted biclusters, FABIA clearly outperformed all 11 competitors. ...
Section 2 introduces the multiplicative bicluster model class. Section 3 describes the model selection (training) algorithm for the new model class. ...
doi:10.1093/bioinformatics/btq227
pmid:20418340
pmcid:PMC2881408
fatcat:qcsflwk6mbd4tb5zr5aulyohii
On Variational Inference in Biclustering Models
2021
International Conference on Machine Learning
In this paper, we develop a theory for the estimation of general biclustering models, where the data is assumed to follow certain statistical distribution with underlying biclustering structure. ...
Furthermore, we study the convergence property of the coordinate ascent variational inference algorithm, where both local and global convergence results have been provided. ...
Acknowledgement The authors sincerely thank the anonymous reviewers and area chair for their constructive comments. ...
dblp:conf/icml/FangL21a
fatcat:bxiwl6rtnffavcn7lgaqffpuwy
Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks
[article]
2022
arXiv
pre-print
The latent block model (LBM) has been proposed as a model-based tool for biclustering. ...
We develop an efficient variational expectation-maximization algorithm by observing that the row and column degrees maximize the objective function in the M step given any probability assignment on the ...
Although developed almost independently with biclustering, the models and algorithms for community detection, and challenges that they face are similar to biclustering. ...
arXiv:2206.08465v1
fatcat:xr5w5gz55bamleaayqfhzazsbi
Sparse Biclustering of Transposable Data
2014
Journal of Computational And Graphical Statistics
In addition, we propose a framework for biclustering based on the matrix-variate normal distribution. ...
We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log likelihood. ...
Acknowledgments We thank the editor, an associate editor, and two reviewers for helpful comments that improved the quality of this manuscript. ...
doi:10.1080/10618600.2013.852554
pmid:25364221
pmcid:PMC4212513
fatcat:d4qhlsdaznh2ddndqxd3lgjdbq
Biclustering Algorithms Based on Metaheuristics: A Review
[article]
2022
arXiv
pre-print
The review focuses on the underlying optimization methods and their main search components: representation, objective function, and variation operators. ...
Although various surveys on biclustering have been proposed, there is a lack of a comprehensive survey on the biclustering problem using metaheuristics. ...
Year Ruiz [19] presented the sequential multi-objective biclustering (SMOB) algorithm for finding biclusters of high quality with large variation. ...
arXiv:2203.16241v1
fatcat:27dbyqnrnnfy5esctvbyho2hom
BOBEA
2022
Proceedings of the Genetic and Evolutionary Computation Conference Companion
We propose an evolutionary algorithm based on a bi-objective approach for the biclustering of the Genome-Wide Association. ...
These studies typically use the most common genetic variation between individuals, the Single Nucleotide Polymorphism (SNP). ...
It is based on a sub-matrix search process corresponding to a subset of rows presenting a consistent model for a certain subset of columns of the data matrix. ...
doi:10.1145/3520304.3528802
fatcat:g3kfonwtpfemdd5o3i45tlbili
Biclustering on expression data: A review
2015
Journal of Biomedical Informatics
based on evaluation measures and non metric-based biclustering algorithms. ...
Nevertheless, not all existing biclustering approaches base their search on evaluation measures for biclusters. ...
For a single bicluster, the same model as in the plaid model [58] is assumed. ...
doi:10.1016/j.jbi.2015.06.028
pmid:26160444
fatcat:3w5p45w4zzebxmnphdue67ueny
An Efficient Method to Improve the Clustering Performance using Hybrid Robust Principal Component Analysis-Spectral biclustering in Rainfall Patterns Identification
2019
IAES International Journal of Artificial Intelligence (IJ-AI)
Evident from this analysis, it is proven that the proposed RPCA-spectral biclustering model is predominantly acclimatized to the identifying rainfall patterns in Peninsular Malaysia due to the small variation ...
The experimental results showed that the best cumulative percentage of variation in between 65% - 70% for both Robust and classical PCA. ...
ACKNOWLEDGEMENTS The authors wish to express gratitude towards Universiti Pendidikan Sultan Idris for their support and financial funding via GPU grant Vote No. 2018-0154-101-01. ...
doi:10.11591/ijai.v8.i3.pp237-243
fatcat:ixh2db3hpbbf5gp4ejlrrgfuhe
A comparative analysis of biclustering algorithms for gene expression data
2012
Briefings in Bioinformatics
However, it is not clear which algorithms are best suited for this task. Many algorithms have been published in the past decade, most of which have been compared only to a small number of algorithms. ...
The algorithms were tested on a suite of synthetic datasets to measure their performance on data with varying conditions, such as different bicluster models, varying noise, varying numbers of biclusters ...
Two factor analysis models are used to fit this model to the data set; variational expectation maximization is used to maximize the posterior. ...
doi:10.1093/bib/bbs032
pmid:22772837
pmcid:PMC3659300
fatcat:r3xctzpbbvayfipgq65jdotu74
Optimisation algorithms for microarray biclustering
2013
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Given a robust weighting scheme proposed in [7] , we modelled the problem as a biclustering problem for which we proposed a propagation heuristic and several variations of a genetic algorithm. ...
[15] for a general review), and a number are variations of the concept of clustering. ...
doi:10.1109/embc.2013.6609569
pmid:24109756
dblp:conf/embc/PerrinD13
fatcat:5a5qon4y4rg63oimkd7qnmlt3e
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