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Francisco de Assis Tenório de Carvalho
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Journal Articles
- 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) - 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) - 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) - 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) - 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) - 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) - 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) - 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) - 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) - 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) - 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) - 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) - 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) - 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) - 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) - 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) - 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) - 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) - 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) - 1995
- [j1]Francisco de A. T. de Carvalho:
Histograms in symbolic data analysis. Ann. Oper. Res. 55(2): 299-322 (1995)
Conference and Workshop Papers
- 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 - 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 - 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
- [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
- [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
- [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
- [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
- [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
- [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
- [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 - 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 - 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 - 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 - 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
- [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
- [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 - 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
- [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
- [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
Parts in Books or Collections
- 2009
- [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 - 2006
- [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
Informal and Other Publications
- 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) - 2012
- [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
- [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)
Coauthor Index
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