default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDFrancisco de A. T. de Carvalho
Francisco de Assis Tenório de Carvalho
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j53]Laura Maria Palomino Mariño, Francisco de Assis Tenório de Carvalho:
Self-organizing maps with adaptive distances for multiple dissimilarity matrices. Mach. Learn. 113(10): 7783-7806 (2024)- 2023
- [j52]Diogo P. P. Branco, Francisco de A. T. de Carvalho
:
Medoid based semi-supervised fuzzy clustering algorithms for multi-view relational data. Fuzzy Sets Syst. 469: 108630 (2023) - [j51]Eduardo C. Simões, Francisco de A. T. de Carvalho:
Gaussian kernel fuzzy c-means with width parameter computation and regularization. Pattern Recognit. 143: 109749 (2023) - 2022
- [j50]Francisco de A. T. de Carvalho, Antonio Irpino, Rosanna Verde, Antonio Balzanella:
Batch Self-Organizing Maps for Distributional Data with an Automatic Weighting of Variables and Components. J. Classif. 39(2): 343-375 (2022) - [j49]Sara Inés Rizo Rodríguez, Francisco de Assis Tenório de Carvalho:
Clustering interval-valued data with adaptive Euclidean and City-Block distances. Expert Syst. Appl. 198: 116774 (2022) - [j48]Marcos de Souza Oliveira, Sérgio Ricardo de Melo Queiroz, Francisco de A. T. de Carvalho:
Unsupervised feature selection method based on iterative similarity graph factorization and clustering by modularity. Expert Syst. Appl. 208: 118092 (2022) - [j47]Laura M. P. Mariño, Francisco de A. T. de Carvalho:
Two weighted c-medoids batch SOM algorithms for dissimilarity data. Inf. Sci. 607: 603-619 (2022) - [j46]Laura M. P. Mariño, Francisco de A. T. de Carvalho:
Vector batch SOM algorithms for multi-view dissimilarity data. Knowl. Based Syst. 258: 109994 (2022) - [c104]José Nataniel A. de Sá, Marcelo Rodrigo Portela Ferreira, Francisco de A. T. de Carvalho:
Kernel-based Fuzzy Co-clustering in Feature Space with Automated Variable Weighting. FUZZ-IEEE 2022: 1-8 - 2021
- [j45]Sara Inés Rizo Rodríguez, Francisco de A. T. de Carvalho:
Fuzzy clustering algorithms with distance metric learning and entropy regularization. Appl. Soft Comput. 113(Part): 107922 (2021) - [j44]Ricardo A. M. da Silva, Francisco de A. T. de Carvalho:
Weighted Clusterwise Linear Regression based on adaptive quadratic form distance. Expert Syst. Appl. 185: 115609 (2021) - [j43]Francisco de A. T. de Carvalho, Eufrasio de Andrade Lima Neto, Ullysses da N. Rosendo:
Interval joint robust regression method. Neurocomputing 465: 265-288 (2021) - [j42]Francisco de A. T. de Carvalho, Antonio Balzanella, Antonio Irpino, Rosanna Verde:
Co-clustering algorithms for distributional data with automated variable weighting. Inf. Sci. 549: 87-115 (2021) - [j41]Francisco de A. T. de Carvalho, Eufrasio de Andrade Lima Neto, Kassio C. F. da Silva:
A clusterwise nonlinear regression algorithm for interval-valued data. Inf. Sci. 555: 357-385 (2021) - [j40]Sara Inés Rizo Rodríguez, Francisco de A. T. de Carvalho:
Soft subspace clustering of interval-valued data with regularizations. Knowl. Based Syst. 227: 107191 (2021) - [i3]Sara Inés Rizo Rodríguez, Francisco de Assis Tenório de Carvalho:
Fuzzy clustering algorithms with distance metric learning and entropy regularization. CoRR abs/2102.09529 (2021) - 2020
- [j39]Rene Pereira de Gusmao, Francisco de Assis Tenório de Carvalho:
PSO for Fuzzy Clustering of Multi-view Relational Data. Int. J. Pattern Recognit. Artif. Intell. 34(9): 2050022:1-2050022:33 (2020) - [c103]Laura M. P. Mariño, Francisco de A. T. de Carvalho:
A new batch SOM algorithm for relational data with weighted medoids. IJCNN 2020: 1-8
2010 – 2019
- 2019
- [j38]Rene Pereira de Gusmao, Francisco de A. T. de Carvalho:
Clustering of multi-view relational data based on particle swarm optimization. Expert Syst. Appl. 123: 34-53 (2019) - [c102]Andréa B. Duque, Francisco de A. T. de Carvalho, Renato Vimieiro:
A Multiview Clustering Approach for Mining Authorial Affinities in Literary Texts. BRACIS 2019: 818-823 - [c101]Sara Inés Rizo Rodríguez, Francisco de Assis Tenório de Carvalho:
A new fuzzy clustering algorithm for interval-valued data based on City-Block distance. FUZZ-IEEE 2019: 1-6 - [c100]Nicomedes L. Cavalcanti, Marcelo Rodrigo Portela Ferreira, Francisco de Assis Tenório de Carvalho:
Adaptive- L_2 L 2 Batch Neural Gas. ICANN (2) 2019: 84-95 - [c99]Sara Inés Rizo Rodríguez, Francisco de Assis Tenório de Carvalho:
Clustering interval-valued data with automatic variables weighting. IJCNN 2019: 1-8 - [c98]Eduardo C. Simões, Francisco de A. T. de Carvalho:
A Fuzzy Clustering Algorithm with Multi-medoids for Multi-view Relational Data. ISNN (1) 2019: 469-477 - 2018
- [j37]Eufrasio de Andrade Lima Neto, Francisco de A. T. de Carvalho:
An exponential-type kernel robust regression model for interval-valued variables. Inf. Sci. 454-455: 419-442 (2018) - [j36]Francisco de A. T. de Carvalho, Eduardo C. Simões, Lucas V. C. Santana, Marcelo Rodrigo Portela Ferreira:
Gaussian kernel c-means hard clustering algorithms with automated computation of the width hyper-parameters. Pattern Recognit. 79: 370-386 (2018) - [c97]Sara Inés Rizo Rodríguez, Francisco de A. T. de Carvalho:
Fuzzy clustering Algorithm based on Adaptive City-block distance and Entropy Regularization. FUZZ-IEEE 2018: 1-8 - [c96]Ricardo A. M. da Silva, Francisco de A. T. de Carvalho:
On Combining Fuzzy C-Regression Models and Fuzzy C-Means with Automated Weighting of the Explanatory Variables. FUZZ-IEEE 2018: 1-8 - [c95]Francisco de A. T. de Carvalho, Lucas V. C. Santana, Marcelo Rodrigo Portela Ferreira:
Gaussian Kernel-Based Fuzzy Clustering with Automatic Bandwidth Computation. ICANN (1) 2018: 685-694 - [c94]Sara Inés Rizo Rodríguez, Francisco de Assis Tenório de Carvalho:
Fuzzy Clustering Algorithm Based on Adaptive Euclidean Distance and Entropy Regularization for Interval-Valued Data. ICANN (1) 2018: 695-705 - 2017
- [j35]Francisco de A. T. de Carvalho, Eufrasio de Andrade Lima Neto, Marcelo Rodrigo Portela Ferreira:
A robust regression method based on exponential-type kernel functions. Neurocomputing 234: 58-74 (2017) - [j34]Francisco de A. T. de Carvalho, Eduardo C. Simões:
Fuzzy clustering of interval-valued data with City-Block and Hausdorff distances. Neurocomputing 266: 659-673 (2017) - [j33]Antonio Irpino, Rosanna Verde, Francisco de A. T. de Carvalho:
Fuzzy clustering of distributional data with automatic weighting of variable components. Inf. Sci. 406: 248-268 (2017) - [j32]Eufrasio de Andrade Lima Neto, Francisco de A. T. de Carvalho:
Nonlinear regression applied to interval-valued data. Pattern Anal. Appl. 20(3): 809-824 (2017) - [c93]Diogo P. P. Branco, Francisco de A. T. de Carvalho:
Fuzzy clustering of multi-view relational data with pairwise constraints. FUZZ-IEEE 2017: 1-6 - [c92]Sara Inés Rizo Rodríguez, Francisco de A. T. de Carvalho:
Fuzzy clustering algorithm with automatic variable selection and entropy regularization. FUZZ-IEEE 2017: 1-6 - [c91]Ricardo A. M. da Silva, Francisco de A. T. de Carvalho:
On Combining Clusterwise Linear Regression and K-Means with Automatic Weighting of the Explanatory Variables. ICANN (2) 2017: 402-410 - [c90]Rodrigo C. de Araujo, Francisco de A. T. de Carvalho, Yves Lechevallier:
Multi-view hard c-means with automated weighting of views and variables. IJCNN 2017: 2792-2799 - 2016
- [j31]Francisco de A. T. de Carvalho, Patrice Bertrand, Eduardo C. Simões:
Batch SOM algorithms for interval-valued data with automatic weighting of the variables. Neurocomputing 182: 66-81 (2016) - [j30]Marcelo Rodrigo Portela Ferreira, Francisco de A. T. de Carvalho, Eduardo C. Simões:
Kernel-based hard clustering methods with kernelization of the metric and automatic weighting of the variables. Pattern Recognit. 51: 310-321 (2016) - [j29]Shun-Feng Su, Witold Pedrycz, Tzung-Pei Hong, Francisco de A. T. de Carvalho:
Guest Editorial Special Issue on Granular/Symbolic Data Processing. IEEE Trans. Cybern. 46(2): 342-343 (2016) - [c89]Francisco de A. T. de Carvalho, Marcelo Rodrigo Portela Ferreira, Eduardo C. Simões:
A Gaussian Kernel-based Clustering Algorithm with Automatic Hyper-parameters Computation. ISNN 2016: 393-400 - [c88]Rene Pereira de Gusmao, Francisco de Assis Tenório de Carvalho:
Particle Swarm Optimization applied to relational data clustering. SMC 2016: 1690-1695 - 2015
- [j28]Francisco de A. T. de Carvalho, Filipe M. de Melo, Yves Lechevallier:
A multi-view relational fuzzy c-medoid vectors clustering algorithm. Neurocomputing 163: 115-123 (2015) - [c87]Francisco de A. T. de Carvalho, Eduardo C. Simões:
A Set-Medoids Vector Batch SOM Algorithm Based on Multiple Dissimilarity Matrices. BRACIS 2015: 180-185 - [c86]Francisco de A. T. de Carvalho, Antonio Irpino, Rosanna Verde:
Fuzzy clustering of distribution-valued data using an adaptive L2 Wasserstein distance. FUZZ-IEEE 2015: 1-8 - [c85]Charlotte Laclau, Francisco de A. T. de Carvalho, Mohamed Nadif:
Fuzzy co-clustering with automated variable weighting. FUZZ-IEEE 2015: 1-8 - 2014
- [j27]Antonio Irpino, Rosanna Verde, Francisco de A. T. de Carvalho:
Dynamic clustering of histogram data based on adaptive squared Wasserstein distances. Expert Syst. Appl. 41(7): 3351-3366 (2014) - [j26]Marcelo Rodrigo Portela Ferreira, Francisco de A. T. de Carvalho:
Kernel fuzzy c-means with automatic variable weighting. Fuzzy Sets Syst. 237: 1-46 (2014) - [j25]Marcelo Rodrigo Portela Ferreira, Francisco de A. T. de Carvalho:
Kernel-based hard clustering methods in the feature space with automatic variable weighting. Pattern Recognit. 47(9): 3082-3095 (2014) - [c84]Valmir Macário Filho, Francisco de Assis Tenório de Carvalho:
An adjustable p-exponential clustering algorithm. ESANN 2014 - [c83]Marcelo Rodrigo Portela Ferreira, Francisco de A. T. de Carvalho:
A kernel k-means clustering algorithm based on an adaptive Mahalanobis kernel. IJCNN 2014: 1885-1892 - 2013
- [j24]Francisco de A. T. de Carvalho, Yves Lechevallier, Filipe M. de Melo:
Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices. Fuzzy Sets Syst. 215: 1-28 (2013) - [j23]Sérgio Ricardo de Melo Queiroz, Francisco de A. T. de Carvalho, Yves Lechevallier:
Nonlinear multicriteria clustering based on multiple dissimilarity matrices. Pattern Recognit. 46(12): 3383-3394 (2013) - [c82]Francisco de A. T. de Carvalho, Filipe M. de Melo, Yves Lechevallier:
A Fuzzy C-Medoids Clustering Algorithm Based on Multiple Dissimilarity Matrices. BRACIS 2013: 107-112 - [c81]Francisco de A. T. de Carvalho, Gibson B. N. Barbosa, Julio T. Pimentel:
Partitioning Fuzzy C-Means Clustering Algorithms for Interval-Valued Data Based on City-Block Distances. BRACIS 2013: 113-118 - [c80]Miloud Bessafi, Francisco de A. T. de Carvalho, Philippe Charton, Mathieu Delsaut, Thierry Despeyroux, Patrick Jeanty, Jean-Daniel Lan Sun Luk, Yves Lechevallier, Henri Ralambondrainy, Lionel Trovalet:
Clustering of Solar Irradiance. ECDA 2013: 43-53 - [c79]Filipe M. de Melo, Francisco de A. T. de Carvalho:
Semi-supervised fuzzy c-medoids clustering algorithm with multiple prototype representation. FUZZ-IEEE 2013 - [c78]Francisco de A. T. de Carvalho, Gibson B. N. Barbosa:
Batch self-organizing maps for mixed feature-type symbolic data. IJCNN 2013: 1-8 - 2012
- [j22]Thaís Gaudencio do Rêgo, Helge G. Roider, Francisco de A. T. de Carvalho, Ivan G. Costa:
Inferring epigenetic and transcriptional regulation during blood cell development with a mixture of sparse linear models. Bioinform. 28(18): 2297-2303 (2012) - [j21]Francisco de A. T. de Carvalho, Yves Lechevallier, Filipe M. de Melo:
Partitioning hard clustering algorithms based on multiple dissimilarity matrices. Pattern Recognit. 45(1): 447-464 (2012) - [c77]Francisco de A. T. de Carvalho, Yves Lechevallier, Thierry Despeyroux, Filipe M. de Melo:
Multi-view Clustering on Relational Data. EGC (best of volume) 2012: 37-51 - [c76]Francisco de A. T. de Carvalho, Filipe M. de Melo, Yves Lechevallier, Thierry Despeyroux:
Un algorithme de classification automatique pour des données relationnelles multivues. EGC 2012: 125-136 - [c75]Francisco de A. T. de Carvalho, Julio T. Pimentel:
A fuzzy clustering algorithm based on adaptive city-block distances. FUZZ-IEEE 2012: 1-8 - [c74]Marcelo Rodrigo Portela Ferreira, Francisco de A. T. de Carvalho:
Kernel fuzzy clustering methods based on local adaptive distances. FUZZ-IEEE 2012: 1-8 - [c73]Valmir Macário Filho, Francisco de A. T. de Carvalho:
An adaptive semi-supervised fuzzy clustering algorithm based on objective function optimization. FUZZ-IEEE 2012: 1-8 - [c72]Sérgio Ricardo de Melo Queiroz, Francisco de A. T. de Carvalho, Yves Lechevallier:
Multicriteria clustering with weighted Tchebycheff distances for relational data. IJCNN 2012: 1-6 - [c71]Francisco de A. T. de Carvalho, Gibson B. N. Barbosa, Marcelo Rodrigo Portela Ferreira:
Variable-Wise Kernel-Based Clustering Algorithms for Interval-Valued Data. SBRN 2012: 25-30 - [c70]Valmir Macário Filho, Ivan G. Costa, João F. L. Oliveira, Francisco de A. T. de Carvalho:
Predicting Gene Functions Using Semi-supervised Clustering Algorithms with Objective Function Optimization. SBRN 2012: 61-66 - [c69]Marcelo Rodrigo Portela Ferreira, Francisco de A. T. de Carvalho:
Partitioning hard kernel clustering methods based on local adaptive distances. SMC 2012: 339-344 - [c68]Alberto Pereira de Barros, Francisco de Assis Tenório de Carvalho, Eufrasio de Andrade Lima Neto:
A pattern classifier for interval-valued data based on multinomial logistic regression model. SMC 2012: 541-546 - [c67]C. A. G. de Araujo Junior, Francisco de A. T. de Carvalho, André Luis Santiago Maia:
Exponential smoothing methods for forecasting bar diagram-valued time series. SMC 2012: 1361-1366 - [c66]Francisco de A. T. de Carvalho, Julio T. Pimentel:
Partitioning fuzzy clustering algorithms for interval-valued data based on Hausdorff distances. SMC 2012: 1379-1384 - [c65]Francisco de A. T. de Carvalho, Lucas F. S. Cambuim:
Partitioning fuzzy clustering algorithms for mixed feature-type symbolic data. SMC 2012: 1385-1390 - [c64]Valmir Macário Filho, Francisco de A. T. de Carvalho:
An adaptive isodata fuzzy clustering algorithm with partial supervision. SMC 2012: 1978-1983 - [i2]Alzennyr Da Silva, Yves Lechevallier, Fabrice Rossi, Francisco de A. T. de Carvalho:
Clustering Dynamic Web Usage Data. CoRR abs/1201.0963 (2012) - 2011
- [j20]Ivan G. Costa, Helge G. Roider, Thaís Gaudencio do Rêgo, Francisco de A. T. de Carvalho:
Predicting gene expression in T cell differentiation from histone modifications and transcription factor binding affinities by linear mixture models. BMC Bioinform. 12(S-1): S29 (2011) - [j19]Byron Leite Dantas Bezerra, Francisco de Assis Tenório de Carvalho:
Symbolic data analysis tools for recommendation systems. Knowl. Inf. Syst. 26(3): 385-418 (2011) - [c63]Marc Csernel, Francisco de Assis Tenório de Carvalho:
Normalizing Constrained Symbolic Data for Clustering. HDSDA 2011: 58-77 - [c62]Anderson B. dos S. Dantas, Francisco de A. T. de Carvalho:
Adaptive Batch SOM for Multiple Dissimilarity Data Tables. ICTAI 2011: 575-578 - [c61]Luciano D. S. Pacífico, Francisco de A. T. de Carvalho:
A batch self-organizing maps algorithm based on adaptive distances. IJCNN 2011: 2297-2304 - [i1]Antonio Irpino, Rosanna Verde, Francisco de A. T. de Carvalho:
Dynamic Clustering of Histogram Data Based on Adaptive Squared Wasserstein Distances. CoRR abs/1110.1462 (2011) - 2010
- [j18]Eufrasio de Andrade Lima Neto, Francisco de A. T. de Carvalho:
Constrained linear regression models for symbolic interval-valued variables. Comput. Stat. Data Anal. 54(2): 333-347 (2010) - [j17]Francisco de A. T. de Carvalho, Camilo P. Tenorio:
Fuzzy K-means clustering algorithms for interval-valued data based on adaptive quadratic distances. Fuzzy Sets Syst. 161(23): 2978-2999 (2010) - [j16]Francisco de A. T. de Carvalho, Renata M. C. R. de Souza:
Unsupervised pattern recognition models for mixed feature-type symbolic data. Pattern Recognit. Lett. 31(5): 430-443 (2010) - [c60]Francisco de A. T. de Carvalho, Gilbert Saporta, Danilo N. Queiroz:
A Clusterwise Center and Range Regression Model for Interval-Valued Data. COMPSTAT 2010: 461-468 - [c59]Francisco de Assis Tenório de Carvalho:
Recent advances in partitioning clustering algorithms for interval-valued data. EGC 2010: 19-20 - [c58]Valmir Macário Filho, Francisco de Assis Tenório de Carvalho:
A new approach for semi-supervised clustering based on Fuzzy C-Means. FUZZ-IEEE 2010: 1-8 - [c57]Francisco de A. T. de Carvalho, Filipe M. de Melo, Yves Lechevallier:
A relational fuzzy c-means clustering algorithm based on multiple dissimilarity matrices. ISDA 2010: 43-48 - [c56]Clerton Ribeiro, Francisco de Assis Tenório de Carvalho, Ivan G. Costa:
Semi-supervised Approach for Finding Cancer Sub-classes on Gene Expression Data. BSB 2010: 25-34
2000 – 2009
- 2009
- [j15]Francisco de A. T. de Carvalho, Yves Lechevallier:
Partitional clustering algorithms for symbolic interval data based on single adaptive distances. Pattern Recognit. 42(7): 1223-1236 (2009) - [j14]Francisco de A. T. de Carvalho, Marc Csernel, Yves Lechevallier:
Clustering constrained symbolic data. Pattern Recognit. Lett. 30(11): 1037-1045 (2009) - [j13]Francisco de A. T. de Carvalho, Yves Lechevallier:
Dynamic Clustering of Interval-Valued Data Based on Adaptive Quadratic Distances. IEEE Trans. Syst. Man Cybern. Part A 39(6): 1295-1306 (2009) - [c55]Alzennyr Da Silva, Yves Lechevallier, Francisco de A. T. de Carvalho:
Vers la simulation et la détection des changements des données évolutives d'usage du Web. EGC 2009: 453-454 - [c54]Rodrigo G. F. Soares, Teresa Bernarda Ludermir, Francisco de A. T. de Carvalho:
An Analysis of Meta-learning Techniques for Ranking Clustering Algorithms Applied to Artificial Data. ICANN (1) 2009: 131-140 - [c53]Eufrasio de Andrade Lima Neto, Gauss Moutinho Cordeiro, Francisco de Assis Tenório de Carvalho, Ulisses Umbelino dos Anjos, Abner Gomes da Costa:
Bivariate Generalized Linear Model for Interval-Valued Variables. IJCNN 2009: 2226-2229 - [c52]Kelly P. Silva, Francisco de A. T. de Carvalho, Marc Csernel:
Clustering of symbolic data using the assignment-prototype algorithm. IJCNN 2009: 2936-2942 - [p5]Alzennyr Da Silva, Yves Lechevallier, Fabrice Rossi, Francisco de A. T. de Carvalho:
Clustering Dynamic Web Usage Data. Innovative Applications in Data Mining 2009: 71-82 - [p4]Alzennyr Da Silva, Yves Lechevallier, Francisco de A. T. de Carvalho:
Comparing Clustering on Symbolic Data. Intelligent Text Categorization and Clustering 2009: 81-94 - 2008
- [j12]Eufrasio de Andrade Lima Neto, Francisco de A. T. de Carvalho:
Centre and Range method for fitting a linear regression model to symbolic interval data. Comput. Stat. Data Anal. 52(3): 1500-1515 (2008) - [j11]André Luis Santiago Maia, Francisco de A. T. de Carvalho, Teresa Bernarda Ludermir:
Forecasting models for interval-valued time series. Neurocomputing 71(16-18): 3344-3352 (2008) - [c51]André Luis Santiago Maia, Francisco de A. T. de Carvalho:
Neural Networks and Exponential Smoothing Models for Symbolic Interval Time Series Processing - Applications in Stock Market. HIS 2008: 326-331 - [c50]Francisco de A. T. de Carvalho, Luciano D. S. Pacífico:
A Weighted Partitioning Dynamic Clustering Algorithm for Quantitative Feature Data Based on Adaptive Euclidean Distances. HIS 2008: 398-403 - [c49]Kelly P. Silva, Rodrigo G. F. Soares, Francisco de A. T. de Carvalho, Teresa Bernarda Ludermir:
Evolving both size and accuracy of RBF networks using Memetic Algorithm. IJCNN 2008: 1938-1944 - [c48]Rodrigo G. F. Soares, Kelly P. Silva, Teresa Bernarda Ludermir, Francisco de A. T. de Carvalho:
An evolutionary approach for the clustering data problem. IJCNN 2008: 1945-1950 - [c47]Kelly P. Silva, Francisco de A. T. de Carvalho, Marc Csernel:
Clustering of symbolic data through a dissimilarity volume based measure. IJCNN 2008: 2865-2871 - [c46]André Luis Santiago Maia, Francisco de A. T. de Carvalho:
Fitting a Least Absolute Deviation Regression Model on Interval-Valued Data. SBIA 2008: 207-216 - [c45]Valmir Macário Filho, Ricardo Bastos Cavalcante Prudêncio, Francisco de A. T. de Carvalho, Leandro R. Torres, Laerte Rodrigues Jr., Marcos G. Lima:
Automatic Information Extraction in Semi-structured Official Journals. SBRN 2008: 51-56 - [c44]Eufrasio de Andrade Lima Neto, Francisco de Assis Tenório de Carvalho:
Nonlinear regression model to symbolic interval-valued variables. SMC 2008: 1247-1252 - 2007
- [j10]Francisco de A. T. de Carvalho:
Fuzzy c-means clustering methods for symbolic interval data. Pattern Recognit. Lett. 28(4): 423-437 (2007) - [c43]Alzennyr Da Silva, Yves Lechevallier, Fabrice Rossi, Francisco de A. T. de Carvalho:
Construction et analyse de résumés de données évolutives : application aux données d'usage du Web. EGC 2007: 539-544 - [c42]Eleonora Ma. Jesus Oliveira, Paulemir G. Campos, Teresa Bernarda Ludermir, Francisco de A. T. de Carvalho, Wilson Rosa de Oliveira:
Application of a Hybrid Classifier to the Recognition of Petrochemical Odors. HIS 2007: 78-83 - [c41]Renata M. C. R. de Souza, Francisco de A. T. de Carvalho:
A Clustering Method for Mixed Feature-Type Symbolic Data using Adaptive Squared Euclidean Distances. HIS 2007: 168-173 - [c40]Camilo P. Tenorio, Francisco de A. T. de Carvalho, Julio T. Pimentel:
A Partitioning Fuzzy Clustering Algorithm for Symbolic Interval Data based on Adaptive Mahalanobis Distances. HIS 2007: 174-179 - [c39]Francisco de A. T. de Carvalho, Julio T. Pimentel, Lucas X. T. Bezerra:
Clustering of symbolic interval data based on a single adaptive L1 distance. IJCNN 2007: 224-229 - [c38]Eufrasio de Andrade Lima Neto, Francisco de A. T. de Carvalho, Jose F. Coelho Neto:
Inequality Constraints in Regression Models to Symbolic Interval Variables. IJCNN 2007: 801-806 - [c37]Alzennyr Da Silva, Yves Lechevallier, Francisco de A. T. de Carvalho:
Analyzing Distance Measures for Symbolic Data Based on Fuzzy Clustering. ISDA 2007: 109-114 - [c36]Alzennyr Da Silva, Yves Lechevallier, Fabrice Rossi, Francisco de A. T. de Carvalho:
Construction and Analysis of Evolving Data Summaries: An Application on Web Usage Data. ISDA 2007: 377-380 - [c35]Francisco de A. T. de Carvalho, Julio T. Pimentel, Lucas X. T. Bezerra, Renata M. C. R. de Souza:
Clustering symbolic interval data based on a single adaptive hausdorff distance. SMC 2007: 451-455 - [c34]Eufrasio de Andrade Lima Neto, Francisco de A. T. de Carvalho, Jose F. Coelho Neto:
Constrained linear regression models for interval-valued data with dependence. SMC 2007: 456-461 - 2006
- [j9]Marie Chavent, Francisco de A. T. de Carvalho, Yves Lechevallier, Rosanna Verde:
New clustering methods for interval data. Comput. Stat. 21(2): 211-229 (2006) - [j8]Francisco de A. T. de Carvalho, Paula Brito, Hans-Hermann Bock:
Dynamic clustering for interval data based on L 2 distance. Comput. Stat. 21(2): 231-250 (2006) - [j7]Francisco de A. T. de Carvalho, Camilo P. Tenorio, Nicomedes L. Cavalcanti Junior:
Partitional fuzzy clustering methods based on adaptive quadratic distances. Fuzzy Sets Syst. 157(21): 2833-2857 (2006) - [j6]Francisco de A. T. de Carvalho, Renata M. C. R. de Souza, Marie Chavent, Yves Lechevallier:
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data. Pattern Recognit. Lett. 27(3): 167-179 (2006) - [c33]Fabrice Rossi, Francisco de A. T. de Carvalho, Yves Lechevallier, Alzennyr Da Silva:
Comparaison de dissimilarité pour l'analyse de l'usage d'un site web. EGC 2006: 409-414 - [c32]Francisco de A. T. de Carvalho, Nicomedes L. Cavalcanti:
Fuzzy Clustering Algorithms for Symbolic Interval Data based on L2 Norm. FUZZ-IEEE 2006: 55-60 - [c31]Alzennyr Da Silva, Francisco de Assis Tenório de Carvalho, Yves Lechevallier, Brigitte Trousse:
Characterizing visitor groups from web data streams. GrC 2006: 389-392 - [c30]Fabio C. D. Silva, Francisco de A. T. de Carvalho, Renata M. C. R. de Souza, Joyce Q. Silva:
A Modal Symbolic Classifier for Interval Data. ICONIP (2) 2006: 50-59 - [c29]André Luis Santiago Maia, Francisco de A. T. de Carvalho, Teresa Bernarda Ludermir:
A Hybrid Model for Symbolic Interval Time Series Forecasting. ICONIP (2) 2006: 934-941 - [c28]Francisco de A. T. de Carvalho:
A Fuzzy Clustering Algorithm for Symbolic Interval Data Based on a Single Adaptive Euclidean Distance. ICONIP (3) 2006: 1012-1021 - [c27]Gecynalda Soares da Silva Gomes, André Luis Santiago Maia, Teresa Bernarda Ludermir, Francisco de A. T. de Carvalho, Aluízio F. R. Araújo:
Hybrid model with dynamic architecture for forecasting time series. IJCNN 2006: 3742-3747 - [c26]Alzennyr Da Silva, Yves Lechevallier, Francisco de A. T. de Carvalho, Brigitte Trousse:
Mining Web Usage Data for Discovering Navigation Clusters. ISCC 2006: 910-915 - [c25]Renata M. C. R. de Souza, Francisco de A. T. de Carvalho, Daniel F. Pizzato:
A Partitioning Method for Mixed Feature-Type Symbolic Data Using a Squared Euclidean Distance. KI 2006: 260-273 - [c24]Francisco de A. T. de Carvalho, Renata M. C. R. de Souza, Lucas X. T. Bezerra:
A dynamical clustering method for symbolic interval data based on a single adaptive Euclidean distance. SBRN 2006: 42-47 - [c23]Francisco de A. T. de Carvalho:
Fuzzy clustering algorithms for symbolic interval data based on adaptive and non-adaptive Euclidean distances. SBRN 2006: 60-65 - [c22]Eufrasio de Andrade Lima Neto, Francisco de A. T. de Carvalho, Lucas X. T. Bezerra:
Linear Regression Methods to Predict Interval-Valued Data. SBRN 2006: 125-130 - [c21]André Luis Santiago Maia, Francisco de A. T. de Carvalho, Teresa Bernarda Ludermir:
Symbolic interval time series forecasting using a hybrid model. SBRN 2006: 202-207 - [c20]Byron L. D. Bezerra, Francisco de A. T. de Carvalho, Valmir Macário Filho:
C^2: : A Collaborative Recommendation System Based on Modal Symbolic User Profile. Web Intelligence 2006: 673-679 - [p3]Fabrice Rossi, Francisco de A. T. de Carvalho, Yves Lechevallier, Alzennyr Da Silva:
Dissimilarities for Web Usage Mining. Data Science and Classification 2006: 39-46 - [p2]Yves Lechevallier, Rosanna Verde, Francisco de A. T. de Carvalho:
Symbolic Clustering of Large Datasets. Data Science and Classification 2006: 193-201 - [p1]Renata M. C. R. de Souza, Francisco de A. T. de Carvalho, Daniel F. Pizzato:
A Dynamic Clustering Method for Mixed Feature-Type Symbolic Data. Data Science and Classification 2006: 203-210 - 2005
- [c19]Nicomedes L. Cavalcanti, Francisco de A. T. de Carvalho:
An Adaptive Fuzzy c-Means Algorithm with the L2 Norm. Australian Conference on Artificial Intelligence 2005: 1138-1141 - [c18]Eufrasio de Andrade Lima Neto, Francisco de A. T. de Carvalho, Eduarda S. Freire:
Applying Constrained Linear Regression Models to Predict Interval-Valued Data. KI 2005: 92-106 - [c17]Luciano Barbosa, Ana Carolina Salgado, Francisco de A. T. de Carvalho, Jacques Robin, Juliana Freire:
Looking at both the present and the past to efficiently update replicas of web content. WIDM 2005: 75-80 - 2004
- [j5]Byron L. D. Bezerra, Francisco de A. T. de Carvalho:
A symbolic approach for content-based information filtering. Inf. Process. Lett. 92(1): 45-52 (2004) - [j4]Renata M. C. R. de Souza, Francisco de A. T. de Carvalho:
Clustering of interval data based on city-block distances. Pattern Recognit. Lett. 25(3): 353-365 (2004) - [j3]Ricardo Bastos Cavalcante Prudêncio, Teresa Bernarda Ludermir, Francisco de A. T. de Carvalho:
A Modal Symbolic Classifier for selecting time series models. Pattern Recognit. Lett. 25(8): 911-921 (2004) - [c16]Eufrasio de Andrade Lima Neto, Francisco de A. T. de Carvalho, Camilo P. Tenorio:
Univariate and Multivariate Linear Regression Methods to Predict Interval-Valued Features. Australian Conference on Artificial Intelligence 2004: 526-537 - [c15]Byron L. D. Bezerra, Francisco de A. T. de Carvalho:
A Symbolic Hybrid Approach to Face the New User Problem in Recommender Systems. Australian Conference on Artificial Intelligence 2004: 1011-1016 - [c14]Byron L. D. Bezerra, Francisco de A. T. de Carvalho, Gustavo Alves:
Collaborative Filtering Based on Modal Symbolic User Profiles: Knowing You in the First Meeting. IBERAMIA 2004: 235-245 - [c13]Renata M. C. R. de Souza, Francisco de A. T. de Carvalho, Camilo P. Tenorio:
Two Partitional Methods for Interval-Valued Data Using Mahalanobis Distances. IBERAMIA 2004: 454-463 - [c12]Simith T. D'Oliveira Junior, Francisco de A. T. de Carvalho, Renata M. C. R. de Souza:
A Classifier for Quantitative Feature Values Based on a Region Oriented Symbolic Approach. IBERAMIA 2004: 464-473 - [c11]Alzennyr Da Silva, Francisco de A. T. de Carvalho, Teresa Bernarda Ludermir, Nicomedes L. Cavalcanti:
Comparing Metrics in Fuzzy Clustering for Symbolic Data on SODAS Format. IBERAMIA 2004: 727-736 - [c10]Simith T. D'Oliveira Junior, Francisco de A. T. de Carvalho, Renata M. C. R. de Souza:
Classification of SAR Images Through a Convex Hull Region Oriented Approach. ICONIP 2004: 769-774 - [c9]Renata M. C. R. de Souza, Francisco de A. T. de Carvalho, Fabio C. D. Silva:
Clustering of Interval-Valued Data Using Adaptive Squared Euclidean Distances. ICONIP 2004: 775-780 - [c8]Francisco de A. T. de Carvalho, Eufrasio de Andrade Lima Neto, Camilo P. Tenorio:
A New Method to Fit a Linear Regression Model for Interval-Valued Data. KI 2004: 295-306 - [c7]Francisco de A. T. de Carvalho, Renata M. C. R. de Souza, Fabio C. D. Silva:
A Clustering Method for Symbolic Interval-Type Data Using Adaptive Chebyshev Distances. SBIA 2004: 266-275 - [c6]Sérgio Ricardo de Melo Queiroz, Francisco de A. T. de Carvalho:
Making Collaborative Group Recommendations Based on Modal Symbolic Data. SBIA 2004: 307-316 - 2002
- [j2]Ivan G. Costa, Francisco de A. T. de Carvalho, Marcílio Carlos Pereira de Souto:
Comparative study on proximity indices for cluster analysis of gene expression time series. J. Intell. Fuzzy Syst. 13(2-4): 133-142 (2002) - [c5]Byron L. D. Bezerra, Francisco de A. T. de Carvalho, Geber L. Ramalho, Jean-Daniel Zucker:
Speeding up Recommender Systems with Meta-prototypes. SBIA 2002: 227-236 - [c4]Ivan R. Teixeira, Francisco de A. T. de Carvalho, Geber L. Ramalho, Vincent Corruble:
ActiveCP: A Method for Speeding up User Preferences Acquisition in Collaborative Filtering Systems. SBIA 2002: 237-247 - [c3]Sérgio Ricardo de Melo Queiroz, Francisco de A. T. de Carvalho, Geber L. Ramalho, Vincent Corruble:
Making Recommendations for Groups Using Collaborative Filtering and Fuzzy Majority. SBIA 2002: 248-258 - [c2]Ivan G. Costa, Francisco de A. T. de Carvalho, Marcílio Carlos Pereira de Souto:
A Symbolic Approach to Gene Expression Time Series Analysis. SBRN 2002: 25-30 - [c1]Ivan G. Costa, Francisco de A. T. de Carvalho, Marcílio Carlos Pereira de Souto:
Stability Evaluation of Clustering Algorithms for Time Series Gene Expression Data. WOB 2002: 88-90
1990 – 1999
- 1995
- [j1]Francisco de A. T. de Carvalho:
Histograms in symbolic data analysis. Ann. Oper. Res. 55(2): 299-322 (1995)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-25 20:15 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
