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On the Approximation of Correlation Clustering and Consensus Clustering

Paola Bonizzoni, Gianluca Della Vedova, Riccardo Dondi, Tao Jiang
2008 Journal of computer and system sciences (Print)  
The Correlation Clustering problem has been introduced recently [N. Bansal, A. Blum, S. Chawla, Correlation Clustering, in: Proc. 43rd Symp.  ...  We are given a correlation graph G = (V , E) and we want to find a partition π of V minimizing the sum e=(i,j ) (r π (i, j )b(i, j ) + (1 − r π (i, j ))a(i, j )). Maximum Correlation Clustering.  ...  Acknowledgments We would like to express our deep appreciation to the anonymous reviewers for their thorough reviews of the manuscript and many very helpful and constructive comments.  ... 
doi:10.1016/j.jcss.2007.06.024 fatcat:t7lm6rvtbfervnkbaiteicq7ye

Hox proteins have different affinities for a consensus DNA site that correlate with the positions of their genes on the hox cluster

I Pellerin, C Schnabel, K M Catron, C Abate
1994 Molecular and Cellular Biology  
Intriguingly, their relative affinities correlate with the positions of their respective genes on the hox cluster.  ...  In this study, we characterized the DNA binding properties of five representative members of the Hox family: HoxA5, HoxB4, HoxA7, HoxC8, and HoxB1.  ...  Rauscher III and Jennifer Morris, Wistar Institute, for the gift of the cytomegalovirus-f3-galactosidase vector; Chuck Kunsch and C.  ... 
doi:10.1128/mcb.14.7.4532 pmid:7911971 pmcid:PMC358825 fatcat:k7ydiws23farfa5ad2aes2uyni

Estimation of relationships between chemical substructures and antibiotic resistance-related gene expression in bacteria: Adapting a canonical correlation analysis for small sample data of gathered features using consensus clustering

Tsuyoshi Esaki, Takaaki Horinouchi, Yayoi Natsume-Kitatani, Yosui Nojima, Iwao Sakane, Hidetoshi Mastsui
2020 Chem-Bio Informatics Journal  
Thus, we combined consensus clustering to gather and reduce features.  ...  It is important to analyze the relationships between phenotypic changes and compound structures; hence, we performed a canonical correlation analysis (CCA) for high dimensional phenotypic and compound  ...  Here, we performed consensus clustering (CC) to gather features of both chemical substructures and phenotypic changes of acquired drug-resistant bacterial strains.  ... 
doi:10.1273/cbij.20.58 fatcat:qlbpwypnjrhkdcqaavvoa2rwzi

Robust consensus clustering for identification of expressed genes linked to malignancy of human colorectal carcinoma

Gatot Wahyudi, Ito Wasito, Tisha Melia, Indra Budi
2011 Bioinformation  
Our proposed algorithm uses multiple clustering algorithms under the consensus clustering framework.  ...  In this study, we propose an enhanced algorithm that provides stability and robustness in identifying differentially expressed genes in an expression profile analysis.  ...  consensus clustering Figure 3 : 3 Correlation of gene expressions with cancer phenotype.  ... 
doi:10.6026/97320630006279 pmid:21738330 pmcid:PMC3124694 fatcat:3ey5df2uefawxf6vgiextpj4j4

Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer

Chintan Parmar, Ralph T. H. Leijenaar, Patrick Grossmann, Emmanuel Rios Velazquez, Johan Bussink, Derek Rietveld, Michelle M. Rietbergen, Benjamin Haibe-Kains, Philippe Lambin, Hugo J.W.L. Aerts
2015 Scientific Reports  
Consensus clustering resulted in eleven and thirteen stable radiomic feature clusters for Lung and H&N cancer, respectively.  ...  Our analysis indicated both common as well as cancer-specific clustering and clinical associations of radiomic features.  ...  Cluster HNCL-10 had moderate cluster consensus (consensus = 0.65) and poor cluster correlation (correlation = 0.04), whereas clusters HNCL-3 showed poor cluster consensus (consensus = 0.41) and correlation  ... 
doi:10.1038/srep11044 pmid:26251068 pmcid:PMC4937496 fatcat:i7dxa6jlrna37agbo75nabknha

Application of Rank Correlation, Clustering and Classification in Information Security

Gleb Beliakov, John Yearwood, Andrei Kelarev
2012 Journal of Networks  
Third, we use a consensus function to combine independent initial clusterings into one consensus clustering.  ...  We investigated various combinations of several correlation coefficients, consensus functions, and a variety of supervised classification algorithms.  ...  Kelarev, "Consensus clustering and supervised classification for profiling Figure 1 . 1 Silhouette indices of the final consensus clusterings SRCC, Kendall Rank Correlation Coefficient, KRCC, and  ... 
doi:10.4304/jnw.7.6.935-945 fatcat:irya7wmw2fatlbkdar6s3okfx4

Can consumer segmentation in projective mapping contribute to a better understanding of consumer perception?

Leticia Vidal, Lucía Antúnez, Ana Giménez, Paula Varela, Rosires Deliza, Gastón Ares
2016 Food Quality and Preference  
and preference patterns. 38 39  ...  In all cases it was 33 observed that the consensus configuration was highly similar to the configuration of one of 34 the groups, which was not necessarily the larger but the one with the highest explained  ...  MFA(%) Correlation between the Clusters' and consensus configuration in the first two dimensions of the MFA Best correlation between the first two dimensions of the Clusters' MFA and two dimensions  ... 
doi:10.1016/j.foodqual.2015.04.008 fatcat:n3blrdagybbtdohqthiiroevny

Towards a consensus for calculating dendrogram-based functional diversity indices

Maud Mouchet, François Guilhaumon, Sébastien Villéger, Norman W. H. Mason, Jean-Antoine Tomasini, David Mouillot
2008 Oikos  
measures and clustering methods.  ...  From artificially generated datasets varying in species richness and correlations between traits, we test whether any single combination of clustering method(s) and distance consistently produces a dendrogram  ...  Acknowledgements Á This study was partially funded by a LITEAU III (PAMPA) and two ANR (GAIUS and AMPHORE) financial supports to study ecological indicators.  ... 
doi:10.1111/j.0030-1299.2008.16594.x fatcat:qoijxm6y5zhermjphyciwolwu4

Voting-based consensus clustering for combining multiple clusterings of chemical structures

Faisal Saeed, Naomie Salim, Ammar Abdo
2012 Journal of Cheminformatics  
The performance of voting-based consensus clustering method outperformed the Ward's method using F-measure and QPI method for both ALOGP and ECFP_4 fingerprints, while the graph-based consensus clustering  ...  So, in this paper, three voting and graph-based consensus clusterings were used for combining multiple clusterings of chemical structures to enhance the ability of separating biologically active molecules  ...  Acknowledgements This work is supported by the Ministry of Higher Education (MOHE) and Research Management Centre (RMC) at the Universiti Teknologi Malaysia (UTM) under Research University Grant Category  ... 
doi:10.1186/1758-2946-4-37 pmid:23244782 pmcid:PMC3541359 fatcat:ui33ookosfewllvhazvupdlvou


2013 International journal on artificial intelligence tools  
Then, similar structures are grouped into clusters using multidimensional scaling and clustering algorithms.  ...  In this paper, we propose two new algorithms, CC-Select and MDS-QA, based on multidimensional scaling and k-means clustering.  ...  We are thankful to Ioan Kosztin, Bogdan Barz, Zhiquan He, Jingfen Zhang, and Jianlin Cheng for valuable suggestions and feedbacks.  ... 
doi:10.1142/s0218213013600063 pmid:24808625 pmcid:PMC4010235 fatcat:3roza4ozjnfe5l7m5qexwftuha


Xiaoxu Han
2007 Computational Systems Bioinformatics  
The algorithm is a consensus kernel hierarchical clustering (CKHC) method in the subspace generated by the PG-NMF.  ...  In this work, we describe a subspace consensus kernel clustering algorithm based on the projected gradient nonnegative matrix factorization (PG-NMF).  ...  Fig. 1 . 1 The visualization of the consensus tree at rank 5 for a Gaussian kernel under the correlation distance and average linkage metric.  ... 
doi:10.1142/9781860948732_0010 fatcat:drscwtsesfcm7iudjcfnztqiva

Are approximation algorithms for consensus clustering worthwhile? [chapter]

Michael Bertolacci, Anthony Wirth
2007 Proceedings of the 2007 SIAM International Conference on Data Mining  
Consensus clustering has emerged as one of the principal clustering problems in the data mining community.  ...  To circumvent this, we sample from the data, run the "slow" algorithms on the sample, and then build a consensus clustering from the seed sample clustering, using a range of techniques.  ...  It can be applied also to rank aggregation, and Correlation and Consensus clustering problem instances.  ... 
doi:10.1137/1.9781611972771.41 dblp:conf/sdm/BertolacciW07 fatcat:hcetvapu6zcsxcjnys62waqnlm

Clustering of the structures by using "snakes-&-dragons" approach, or correlation matrix as a signal

Victor P. Andreev, Gang Liu, Jarcy Zee, Lisa Henn, Gilberto E. Flores, Robert M. Merion, Satoru Hayasaka
2019 PLoS ONE  
Importantly, this approach of clustering correlation matrices is different from clustering elements of the correlation matrices, because our goal is to compare and cluster multiple networks-not the nodes  ...  A novel approach for clustering correlation matrices, named "snakes-&-dragons," is introduced and illustrated by examples from neuroscience, human microbiome, and macroeconomics.  ...  Martinos Center for Biomedical Imaging, and GSP Open Access Documentation the Center for Human Genetic Research.  ... 
doi:10.1371/journal.pone.0223267 pmid:31600337 pmcid:PMC6786638 fatcat:dy7oemyykbbnzfixnjtscxtvwu

Exploiting Correlation Consensus

Yang Wang, Xuemin Lin, Lin Wu, Wenjie Zhang, Qing Zhang
2014 Proceedings of the ACM International Conference on Multimedia - MM '14  
In this paper, we study subspace clustering for multi-modal data by effectively exploiting data correlation consensus across modalities, while keeping individual modalities well encapsulated.  ...  As a result, the sparse code vectors of the same cross-modal data have small angular difference so as to achieve the data correlation consensus simultaneously.  ...  cluster, we propose to exploit the data correlation consensus w.r.t. modalities, which critically determines the subspace clustering on multi-modal data.  ... 
doi:10.1145/2647868.2654999 dblp:conf/mm/WangLWZZ14 fatcat:fwdfqbv5prhqjbnkyi5cfwpvca

Consensus Coexpression Network Analysis Identifies Key Regulators of Flower and Fruit Development in Wild Strawberry

Rachel Shahan, Christopher Zawora, Haley Wight, John Sittmann, Wanpeng Wang, Stephen M. Mount, Zhongchi Liu
2018 Plant Physiology  
Both the standard WGCNA and consensus methods performed similarly with regard to the RS statistic and intracluster correlation (Fig. 4A ).  ...  This cluster is also positively correlated with the cortex and pith tissues of the receptacle.  ... 
doi:10.1104/pp.18.00086 pmid:29991484 pmcid:PMC6130042 fatcat:idm4j4enk5arfm6y7qtkx4iuye
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