Year |
Citation |
Score |
2020 |
Cariou V, Alexandre‐Gouabau M, Wilderjans TF. Three‐way clustering around latent variables approach with constraints on the configurations to facilitate interpretation Journal of Chemometrics. DOI: 10.1002/Cem.3269 |
0.343 |
|
2019 |
Durieux J, Wilderjans TF. Partitioning subjects based on high-dimensional fMRI data: comparison of several clustering methods and studying the influence of ICA data reduction in big data Behaviormetrika. 46: 271-311. DOI: 10.1007/S41237-019-00086-4 |
0.395 |
|
2018 |
Waaijenborg S, Korobko O, Dijk KWv, Lips M, Hankemeier T, Wilderjans TF, Smilde AK, Westerhuis JA. Fusing metabolomics data sets with heterogeneous measurement errors Plos One. 13. PMID 29698490 DOI: 10.1371/Journal.Pone.0195939 |
0.31 |
|
2017 |
Cariou V, Wilderjans TF. Consumer segmentation in multi-attribute product evaluation by means of non-negatively constrained CLV3W Food Quality and Preference. 67: 18-26. DOI: 10.1016/J.Foodqual.2017.01.006 |
0.361 |
|
2017 |
Doove LL, Wilderjans TF, Calcagnì A, Mechelen IV. Deriving optimal data-analytic regimes from benchmarking studies Computational Statistics & Data Analysis. 107: 81-91. DOI: 10.1016/J.Csda.2016.10.016 |
0.391 |
|
2016 |
Wilderjans TF, Vande Gaer E, Kiers HA, Van Mechelen I, Ceulemans E. Principal Covariates Clusterwise Regression (PCCR): Accounting for Multicollinearity and Population Heterogeneity in Hierarchically Organized Data. Psychometrika. PMID 27905056 DOI: 10.1007/S11336-016-9522-0 |
0.37 |
|
2015 |
Ceulemans E, Wilderjans TF, Kiers HA, Timmerman ME. MultiLevel simultaneous component analysis: A computational shortcut and software package. Behavior Research Methods. PMID 26170054 DOI: 10.3758/S13428-015-0626-8 |
0.365 |
|
2014 |
Schouteden M, Van Deun K, Wilderjans TF, Van Mechelen I. Performing DISCO-SCA to search for distinctive and common information in linked data. Behavior Research Methods. 46: 576-87. PMID 24178130 DOI: 10.3758/S13428-013-0374-6 |
0.368 |
|
2014 |
Wilderjans TF, Cariou V. CLV3W: A clustering around latent variables approach to detect panel disagreement in three-way conventional sensory profiling data Food Quality and Preference. DOI: 10.1016/J.Foodqual.2015.03.013 |
0.391 |
|
2014 |
Rodríguez SD, Barletta DA, Wilderjans TF, Bernik DL. Fast and Efficient Food Quality Control Using Electronic Noses: Adulteration Detection Achieved by Unfolded Cluster Analysis Coupled with Time-Window Selection Food Analytical Methods. 7: 2042-2050. DOI: 10.1007/S12161-014-9841-7 |
0.369 |
|
2013 |
Bulteel K, Wilderjans TF, Tuerlinckx F, Ceulemans E. CHull as an alternative to AIC and BIC in the context of mixtures of factor analyzers. Behavior Research Methods. 45: 782-91. PMID 23307573 DOI: 10.3758/S13428-012-0293-Y |
0.408 |
|
2013 |
Wilderjans TF, Ceulemans E, Meers K. CHull: a generic convex-hull-based model selection method. Behavior Research Methods. 45: 1-15. PMID 23055156 DOI: 10.3758/S13428-012-0238-5 |
0.343 |
|
2013 |
Rodríguez SD, Wilderjans TF, Sosa N, Bernik DL. Image Texture Analysis and Gas Sensor Array Studies Applied to Vanilla Encapsulation by Octenyl Succinic Anhydride Starches Journal of Field Robotics. 2: 36-48. DOI: 10.5539/Jfr.V2N2P36 |
0.301 |
|
2013 |
Wilderjans TF, Ceulemans E. Clusterwise Parafac to identify heterogeneity in three-way data Chemometrics and Intelligent Laboratory Systems. 129: 87-97. DOI: 10.1016/J.Chemolab.2013.09.010 |
0.403 |
|
2013 |
Wilderjans TF, Depril D, van Mechelen I. Additive Biclustering: A Comparison of One New and Two Existing ALS Algorithms Journal of Classification. 30: 56-74. DOI: 10.1007/S00357-013-9120-0 |
0.329 |
|
2012 |
Wilderjans TF, Ceulemans E, Kuppens P. Clusterwise HICLAS: a generic modeling strategy to trace similarities and differences in multiblock binary data. Behavior Research Methods. 44: 532-45. PMID 22083659 DOI: 10.3758/S13428-011-0166-9 |
0.452 |
|
2012 |
Wilderjans TF, Ceulemans E, van Mechelen I. The SIMCLAS Model: Simultaneous Analysis of Coupled Binary Data Matrices with Noise Heterogeneity Between and Within Data Blocks Psychometrika. 77: 724-740. DOI: 10.1007/S11336-012-9275-3 |
0.376 |
|
2012 |
Wilderjans TF, Depril D, van Mechelen I. Block-Relaxation Approaches for Fitting the INDCLUS Model Journal of Classification. 29: 277-296. DOI: 10.1007/S00357-012-9113-4 |
0.369 |
|
2012 |
Depril D, van Mechelen I, Wilderjans TF. Lowdimensional Additive Overlapping Clustering Journal of Classification. 29: 297-320. DOI: 10.1007/S00357-012-9112-5 |
0.42 |
|
2011 |
Van Deun K, Wilderjans TF, van den Berg RA, Antoniadis A, Van Mechelen I. A flexible framework for sparse simultaneous component based data integration. Bmc Bioinformatics. 12: 448. PMID 22085701 DOI: 10.1186/1471-2105-12-448 |
0.39 |
|
2011 |
Wilderjans TF, Ceulemans E, Van Mechelen I, van den Berg RA. Simultaneous analysis of coupled data matrices subject to different amounts of noise. The British Journal of Mathematical and Statistical Psychology. 64: 277-90. PMID 21492133 DOI: 10.1348/000711010X513263 |
0.381 |
|
2011 |
Wilderjans TF, Ceulemans E, Van Mechelen I, Depril D. ADPROCLUS: a graphical user interface for fitting additive profile clustering models to object by variable data matrices. Behavior Research Methods. 43: 56-65. PMID 21287114 DOI: 10.3758/S13428-010-0033-0 |
0.404 |
|
2009 |
Wilderjans TF, Ceulemans E, Kiers HA, Meers K. The LMPCA program: a graphical user interface for fitting the linked-mode PARAFAC-PCA model to coupled real-valued data. Behavior Research Methods. 41: 1073-82. PMID 19897815 DOI: 10.3758/Brm.41.4.1073 |
0.327 |
|
2009 |
van den Berg RA, Van Mechelen I, Wilderjans TF, Van Deun K, Kiers HA, Smilde AK. Integrating functional genomics data using maximum likelihood based simultaneous component analysis. Bmc Bioinformatics. 10: 340. PMID 19835617 DOI: 10.1186/1471-2105-10-340 |
0.389 |
|
2009 |
Wilderjans T, Ceulemans E, Mechelen IV. Simultaneous analysis of coupled data blocks differing in size: A comparison of two weighting schemes Computational Statistics & Data Analysis. 53: 1086-1098. DOI: 10.1016/J.Csda.2008.09.031 |
0.402 |
|
2008 |
Wilderjans T, Ceulemans E, Mechelen IV. The CHIC Model: A Global Model for Coupled Binary Data Psychometrika. 73: 729-751. DOI: 10.1007/S11336-008-9069-9 |
0.355 |
|
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