Funclust: A curves clustering method using functional random variables density approximation
- Author
- JACQUES, Julien1 2 3 ; PREDA, Cristian1 2 3
[1] Laboratoire Paul Painlevé, UMR CNRS 8524, University Lille I, Lille, France
[2] MODAL team, INRIA Lille-Nord Europe, France
[3] Polytech'Lille, France - Issue title
- Advances in artificial neural networks, machine learning, and computational intelligence: Selected papers from the 20th European Symposium on Artificial Neural Networks (ESSANN 2012)
- Author (monograph)
- SCHLEIF, Frank-Michael (Editor)1 ; MICHELI, Alessio (Editor)2 ; TINO, Peter (Editor)3
[1] University of Bielefeld, Center of Excellence, Theoretical Computer Science, Universitätsstrasse 21-23, 33615 Bielefeld, Germany
[2] University of Pisa, Department of Computer Science, Largo B. Pontecorvo 3, Pisa, Italy
[3] The University of Birmingham, School of Computer Science, Edgbaston, Birmingham B15 2TT, United Kingdom - Source
Neurocomputing (Amsterdam). 2013, Vol 112, pp 164-171, 8 p ; ref : 50 ref
- ISSN
- 0925-2312
- Scientific domain
- Cognition; Computer science
- Publisher
- Elsevier, Amsterdam
- Publication country
- Netherlands
- Document type
- Article
- Language
- English
- Author keyword
- Functional data Functional principal component analysis Model-based clustering Random variable density
- Keyword (fr)
- Ajustement modèle Algorithme EM Amas Analyse amas Analyse composante principale Analyse donnée Analyse fonctionnelle Application spatiale Approximation stochastique Densité probabilité Efficacité Estimation paramètre Fouille donnée Identification système Loi a posteriori Modélisation Mélange loi probabilité Métrologie surface Processus stochastique Système paramètre réparti Variable aléatoire
- Keyword (en)
- Model matching EM algorithm Cluster Cluster analysis Principal component analysis Data analysis Functional analysis Space application Stochastic approximation Probability density Efficiency Parameter estimation Data mining System identification Posterior distribution Modeling Mixed distribution Surface metrology Stochastic process Distributed parameter system Random variable
- Keyword (es)
- Ajustamiento modelo Algoritmo EM Montón Analisis cluster Análisis componente principal Análisis datos Análisis funcional Aplicación espacial Aproximación estocástica Densidad probabilidad Eficacia Estimación parámetro Busca dato Identificación sistema Ley a posteriori Modelización Mezcla ley probabilidad Metrología superficie Proceso estocástico Sistema parámetro repartido Variable aléatoria
- Classification
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D02 Computer science; control theory; systems / 001D02B Software / 001D02B07 Memory organisation. Data processing / 001D02B07B Data processing. List processing. Character string processing
- Discipline
- Computer science : theoretical automation and systems
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 27454433
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Access to the document
Install on your browser
DOI
10.1016/j.neucom.2012.11.042 
https://dx.doi.org/10.1016/j.neucom.2012.11.042
"O:13:\"PanistOpenUrl\":36:{s:10:\"\u0000*\u0000openUrl\";N;s:6:\"\u0000*\u0000idc\";N;s:6:\"\u0000*\u0000fmt\";s:7:\"journal\";s:6:\"\u0000*\u0000doi\";s:28:\"10.1016\/j.neucom.2012.11.042\";s:6:\"\u0000*\u0000pii\";s:0:\"\";s:7:\"\u0000*\u0000pmid\";s:0:\"\";s:9:\"\u0000*\u0000atitle\";s:92:\"Funclust: A curves clustering method using functional random variables density approximation\";s:9:\"\u0000*\u0000jtitle\";s:26:\"Neurocomputing (Amsterdam)\";s:9:\"\u0000*\u0000stitle\";s:22:\"Neurocomputing (Amst.)\";s:7:\"\u0000*\u0000date\";s:4:\"2013\";s:9:\"\u0000*\u0000volume\";s:3:\"112\";s:8:\"\u0000*\u0000issue\";s:0:\"\";s:8:\"\u0000*\u0000spage\";s:3:\"164\";s:8:\"\u0000*\u0000epage\";s:3:\"171\";s:8:\"\u0000*\u0000pages\";s:0:\"\";s:7:\"\u0000*\u0000issn\";s:9:\"0925-2312\";s:8:\"\u0000*\u0000eissn\";s:0:\"\";s:9:\"\u0000*\u0000aulast\";s:7:\"JACQUES\";s:10:\"\u0000*\u0000aufirst\";s:6:\"Julien\";s:9:\"\u0000*\u0000auinit\";s:0:\"\";s:10:\"\u0000*\u0000auinitm\";s:0:\"\";s:5:\"\u0000*\u0000au\";a:1:{i:0;s:15:\"PREDA, Cristian\";}s:9:\"\u0000*\u0000aucorp\";s:0:\"\";s:7:\"\u0000*\u0000isbn\";s:0:\"\";s:8:\"\u0000*\u0000coden\";s:0:\"\";s:8:\"\u0000*\u0000genre\";s:7:\"article\";s:7:\"\u0000*\u0000part\";s:0:\"\";s:9:\"\u0000*\u0000btitle\";s:182:\"Advances in artificial neural networks, machine learning, and computational intelligence: Selected papers from the 20th European Symposium on Artificial Neural Networks (ESSANN 2012)\";s:8:\"\u0000*\u0000title\";s:92:\"Funclust: A curves clustering method using functional random variables density approximation\";s:8:\"\u0000*\u0000place\";s:9:\"Amsterdam\";s:6:\"\u0000*\u0000pub\";s:8:\"Elsevier\";s:10:\"\u0000*\u0000edition\";s:0:\"\";s:9:\"\u0000*\u0000tpages\";s:1:\"8\";s:9:\"\u0000*\u0000series\";s:0:\"\";s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";b:1;}"
Panist access
"O:12:\"IstexOpenUrl\":35:{s:10:\"\u0000*\u0000openUrl\";N;s:6:\"\u0000*\u0000fmt\";s:7:\"journal\";s:6:\"\u0000*\u0000doi\";s:28:\"10.1016\/j.neucom.2012.11.042\";s:6:\"\u0000*\u0000pii\";s:0:\"\";s:7:\"\u0000*\u0000pmid\";s:0:\"\";s:9:\"\u0000*\u0000atitle\";s:92:\"Funclust: A curves clustering method using functional random variables density approximation\";s:9:\"\u0000*\u0000jtitle\";s:26:\"Neurocomputing (Amsterdam)\";s:9:\"\u0000*\u0000stitle\";s:22:\"Neurocomputing (Amst.)\";s:7:\"\u0000*\u0000date\";s:4:\"2013\";s:9:\"\u0000*\u0000volume\";s:3:\"112\";s:8:\"\u0000*\u0000issue\";s:0:\"\";s:8:\"\u0000*\u0000spage\";s:3:\"164\";s:8:\"\u0000*\u0000epage\";s:3:\"171\";s:8:\"\u0000*\u0000pages\";s:0:\"\";s:7:\"\u0000*\u0000issn\";s:9:\"0925-2312\";s:8:\"\u0000*\u0000eissn\";s:0:\"\";s:9:\"\u0000*\u0000aulast\";s:7:\"JACQUES\";s:10:\"\u0000*\u0000aufirst\";s:6:\"Julien\";s:9:\"\u0000*\u0000auinit\";s:0:\"\";s:10:\"\u0000*\u0000auinitm\";s:0:\"\";s:5:\"\u0000*\u0000au\";a:1:{i:0;s:15:\"PREDA, Cristian\";}s:9:\"\u0000*\u0000aucorp\";s:0:\"\";s:7:\"\u0000*\u0000isbn\";s:0:\"\";s:8:\"\u0000*\u0000coden\";s:0:\"\";s:8:\"\u0000*\u0000genre\";s:7:\"article\";s:7:\"\u0000*\u0000part\";s:0:\"\";s:9:\"\u0000*\u0000btitle\";s:182:\"Advances in artificial neural networks, machine learning, and computational intelligence: Selected papers from the 20th European Symposium on Artificial Neural Networks (ESSANN 2012)\";s:8:\"\u0000*\u0000title\";s:92:\"Funclust: A curves clustering method using functional random variables density approximation\";s:8:\"\u0000*\u0000place\";s:9:\"Amsterdam\";s:6:\"\u0000*\u0000pub\";s:8:\"Elsevier\";s:10:\"\u0000*\u0000edition\";s:0:\"\";s:9:\"\u0000*\u0000tpages\";s:1:\"8\";s:9:\"\u0000*\u0000series\";s:0:\"\";s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";b:1;}"
Istex access
"O:16:\"UnpaywallOpenUrl\":35:{s:8:\"\u0000*\u0000email\";s:27:\"assistance-portail@inist.fr\";s:6:\"\u0000*\u0000fmt\";N;s:6:\"\u0000*\u0000doi\";s:28:\"10.1016\/j.neucom.2012.11.042\";s:6:\"\u0000*\u0000pii\";N;s:7:\"\u0000*\u0000pmid\";N;s:9:\"\u0000*\u0000atitle\";N;s:9:\"\u0000*\u0000jtitle\";N;s:9:\"\u0000*\u0000stitle\";N;s:7:\"\u0000*\u0000date\";N;s:9:\"\u0000*\u0000volume\";N;s:8:\"\u0000*\u0000issue\";N;s:8:\"\u0000*\u0000spage\";N;s:8:\"\u0000*\u0000epage\";N;s:8:\"\u0000*\u0000pages\";N;s:7:\"\u0000*\u0000issn\";N;s:8:\"\u0000*\u0000eissn\";N;s:9:\"\u0000*\u0000aulast\";N;s:10:\"\u0000*\u0000aufirst\";N;s:9:\"\u0000*\u0000auinit\";N;s:10:\"\u0000*\u0000auinitm\";N;s:5:\"\u0000*\u0000au\";N;s:9:\"\u0000*\u0000aucorp\";N;s:7:\"\u0000*\u0000isbn\";N;s:8:\"\u0000*\u0000coden\";N;s:8:\"\u0000*\u0000genre\";N;s:7:\"\u0000*\u0000part\";N;s:9:\"\u0000*\u0000btitle\";N;s:8:\"\u0000*\u0000title\";N;s:8:\"\u0000*\u0000place\";N;s:6:\"\u0000*\u0000pub\";N;s:10:\"\u0000*\u0000edition\";N;s:9:\"\u0000*\u0000tpages\";N;s:9:\"\u0000*\u0000series\";N;s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";N;}"
Unpaywall access
"O:16:\"EuropePMCOpenUrl\":35:{s:10:\"\u0000*\u0000openUrl\";N;s:6:\"\u0000*\u0000fmt\";s:7:\"journal\";s:6:\"\u0000*\u0000doi\";s:28:\"10.1016\/j.neucom.2012.11.042\";s:6:\"\u0000*\u0000pii\";s:0:\"\";s:7:\"\u0000*\u0000pmid\";s:0:\"\";s:9:\"\u0000*\u0000atitle\";s:92:\"Funclust: A curves clustering method using functional random variables density approximation\";s:9:\"\u0000*\u0000jtitle\";s:26:\"Neurocomputing (Amsterdam)\";s:9:\"\u0000*\u0000stitle\";s:22:\"Neurocomputing (Amst.)\";s:7:\"\u0000*\u0000date\";s:4:\"2013\";s:9:\"\u0000*\u0000volume\";s:3:\"112\";s:8:\"\u0000*\u0000issue\";s:0:\"\";s:8:\"\u0000*\u0000spage\";s:3:\"164\";s:8:\"\u0000*\u0000epage\";s:3:\"171\";s:8:\"\u0000*\u0000pages\";s:0:\"\";s:7:\"\u0000*\u0000issn\";s:9:\"0925-2312\";s:8:\"\u0000*\u0000eissn\";s:0:\"\";s:9:\"\u0000*\u0000aulast\";s:7:\"JACQUES\";s:10:\"\u0000*\u0000aufirst\";s:6:\"Julien\";s:9:\"\u0000*\u0000auinit\";s:0:\"\";s:10:\"\u0000*\u0000auinitm\";s:0:\"\";s:5:\"\u0000*\u0000au\";a:1:{i:0;s:15:\"PREDA, Cristian\";}s:9:\"\u0000*\u0000aucorp\";s:0:\"\";s:7:\"\u0000*\u0000isbn\";s:0:\"\";s:8:\"\u0000*\u0000coden\";s:0:\"\";s:8:\"\u0000*\u0000genre\";s:7:\"article\";s:7:\"\u0000*\u0000part\";s:0:\"\";s:9:\"\u0000*\u0000btitle\";s:182:\"Advances in artificial neural networks, machine learning, and computational intelligence: Selected papers from the 20th European Symposium on Artificial Neural Networks (ESSANN 2012)\";s:8:\"\u0000*\u0000title\";s:92:\"Funclust: A curves clustering method using functional random variables density approximation\";s:8:\"\u0000*\u0000place\";s:9:\"Amsterdam\";s:6:\"\u0000*\u0000pub\";s:8:\"Elsevier\";s:10:\"\u0000*\u0000edition\";s:0:\"\";s:9:\"\u0000*\u0000tpages\";s:1:\"8\";s:9:\"\u0000*\u0000series\";s:0:\"\";s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";b:1;}"
Europe PubMed Central access
"O:11:\"BaseOpenUrl\":35:{s:10:\"\u0000*\u0000openUrl\";N;s:6:\"\u0000*\u0000fmt\";s:7:\"journal\";s:6:\"\u0000*\u0000doi\";s:28:\"10.1016\/j.neucom.2012.11.042\";s:6:\"\u0000*\u0000pii\";s:0:\"\";s:7:\"\u0000*\u0000pmid\";s:0:\"\";s:9:\"\u0000*\u0000atitle\";s:92:\"Funclust: A curves clustering method using functional random variables density approximation\";s:9:\"\u0000*\u0000jtitle\";s:26:\"Neurocomputing (Amsterdam)\";s:9:\"\u0000*\u0000stitle\";s:22:\"Neurocomputing (Amst.)\";s:7:\"\u0000*\u0000date\";s:4:\"2013\";s:9:\"\u0000*\u0000volume\";s:3:\"112\";s:8:\"\u0000*\u0000issue\";s:0:\"\";s:8:\"\u0000*\u0000spage\";s:3:\"164\";s:8:\"\u0000*\u0000epage\";s:3:\"171\";s:8:\"\u0000*\u0000pages\";s:0:\"\";s:7:\"\u0000*\u0000issn\";s:9:\"0925-2312\";s:8:\"\u0000*\u0000eissn\";s:0:\"\";s:9:\"\u0000*\u0000aulast\";s:7:\"JACQUES\";s:10:\"\u0000*\u0000aufirst\";s:6:\"Julien\";s:9:\"\u0000*\u0000auinit\";s:0:\"\";s:10:\"\u0000*\u0000auinitm\";s:0:\"\";s:5:\"\u0000*\u0000au\";a:1:{i:0;s:15:\"PREDA, Cristian\";}s:9:\"\u0000*\u0000aucorp\";s:0:\"\";s:7:\"\u0000*\u0000isbn\";s:0:\"\";s:8:\"\u0000*\u0000coden\";s:0:\"\";s:8:\"\u0000*\u0000genre\";s:7:\"article\";s:7:\"\u0000*\u0000part\";s:0:\"\";s:9:\"\u0000*\u0000btitle\";s:182:\"Advances in artificial neural networks, machine learning, and computational intelligence: Selected papers from the 20th European Symposium on Artificial Neural Networks (ESSANN 2012)\";s:8:\"\u0000*\u0000title\";s:92:\"Funclust: A curves clustering method using functional random variables density approximation\";s:8:\"\u0000*\u0000place\";s:9:\"Amsterdam\";s:6:\"\u0000*\u0000pub\";s:8:\"Elsevier\";s:10:\"\u0000*\u0000edition\";s:0:\"\";s:9:\"\u0000*\u0000tpages\";s:1:\"8\";s:9:\"\u0000*\u0000series\";s:0:\"\";s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";b:1;}"
Base Search access
"O:12:\"ArXivOpenUrl\":35:{s:10:\"\u0000*\u0000openUrl\";N;s:6:\"\u0000*\u0000fmt\";s:7:\"journal\";s:6:\"\u0000*\u0000doi\";s:28:\"10.1016\/j.neucom.2012.11.042\";s:6:\"\u0000*\u0000pii\";s:0:\"\";s:7:\"\u0000*\u0000pmid\";s:0:\"\";s:9:\"\u0000*\u0000atitle\";s:92:\"Funclust: A curves clustering method using functional random variables density approximation\";s:9:\"\u0000*\u0000jtitle\";s:26:\"Neurocomputing (Amsterdam)\";s:9:\"\u0000*\u0000stitle\";s:22:\"Neurocomputing (Amst.)\";s:7:\"\u0000*\u0000date\";s:4:\"2013\";s:9:\"\u0000*\u0000volume\";s:3:\"112\";s:8:\"\u0000*\u0000issue\";s:0:\"\";s:8:\"\u0000*\u0000spage\";s:3:\"164\";s:8:\"\u0000*\u0000epage\";s:3:\"171\";s:8:\"\u0000*\u0000pages\";s:0:\"\";s:7:\"\u0000*\u0000issn\";s:9:\"0925-2312\";s:8:\"\u0000*\u0000eissn\";s:0:\"\";s:9:\"\u0000*\u0000aulast\";s:7:\"JACQUES\";s:10:\"\u0000*\u0000aufirst\";s:6:\"Julien\";s:9:\"\u0000*\u0000auinit\";s:0:\"\";s:10:\"\u0000*\u0000auinitm\";s:0:\"\";s:5:\"\u0000*\u0000au\";a:1:{i:0;s:15:\"PREDA, Cristian\";}s:9:\"\u0000*\u0000aucorp\";s:0:\"\";s:7:\"\u0000*\u0000isbn\";s:0:\"\";s:8:\"\u0000*\u0000coden\";s:0:\"\";s:8:\"\u0000*\u0000genre\";s:7:\"article\";s:7:\"\u0000*\u0000part\";s:0:\"\";s:9:\"\u0000*\u0000btitle\";s:182:\"Advances in artificial neural networks, machine learning, and computational intelligence: Selected papers from the 20th European Symposium on Artificial Neural Networks (ESSANN 2012)\";s:8:\"\u0000*\u0000title\";s:92:\"Funclust: A curves clustering method using functional random variables density approximation\";s:8:\"\u0000*\u0000place\";s:9:\"Amsterdam\";s:6:\"\u0000*\u0000pub\";s:8:\"Elsevier\";s:10:\"\u0000*\u0000edition\";s:0:\"\";s:9:\"\u0000*\u0000tpages\";s:1:\"8\";s:9:\"\u0000*\u0000series\";s:0:\"\";s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";b:1;}"
arXiv access
