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Une approche basée sur les motifs fermés pour résoudre le problème de clustering par consensus

Author: Atheer Al-NajdiFrédéric Precioso, professeur des universités).Andrea TettamanziKarell BertetPhilippe Fournier-VigerAll authors
Publisher: 2016.
Dissertation: Thèse de doctorat : Informatique : Université Côte d'Azur (ComUE) : 2016.
Edition/Format:   Computer file : Document : Thesis/dissertation : English
Summary:
Le clustering est le processus de partitionnement d'un ensemble de données en groupes, de sorte que les instances du même groupe sont plus semblables les unes aux autres qu'avec celles de tout autre groupe. De nombreux algorithmes de clustering ont été proposés, mais aucun d'entre eux ne s'avère fournir une partitiondes données pertinente dans toutes les situations. Le clustering par consensus vise à
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Details

Genre/Form: Thèses et écrits académiques
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Atheer Al-Najdi; Frédéric Precioso, professeur des universités).; Andrea Tettamanzi; Karell Bertet; Philippe Fournier-Viger; Arnaud Revel; Nicolas Pasquier; Sanghamitra Bandyopadhyay; Université Côte d'Azur (2015-2019).; École doctorale Sciences et technologies de l'information et de la communication (Sophia Antipolis, Alpes-Maritimes).; Université de Nice (1965-2019).; Laboratoire Informatique, signaux et systèmes (Sophia Antipolis, Alpes-Maritimes).
OCLC Number: 973925459
Notes: Titre provenant de l'écran-titre.
Description: 1 online resource
Responsibility: Atheer Al-Najdi ; sous la direction de Frédéric Precioso.

Abstract:

Le clustering est le processus de partitionnement d'un ensemble de données en groupes, de sorte que les instances du même groupe sont plus semblables les unes aux autres qu'avec celles de tout autre groupe. De nombreux algorithmes de clustering ont été proposés, mais aucun d'entre eux ne s'avère fournir une partitiondes données pertinente dans toutes les situations. Le clustering par consensus vise à améliorer le processus de regroupement en combinant différentes partitions obtenues à partir de divers algorithmes afin d'obtenir une solution de consensus de meilleure qualité. Dans ce travail, une nouvelle méthode de clustering par consensus, appelée MultiCons, est proposée. Cette méthode utilise la technique d'extraction des itemsets fréquents fermés dans le but de découvrir les similitudes entre les différentes solutions de clustering dits de base. Les similitudes identifiées sont représentées sous une forme de motifs de clustering, chacun définissant un accord entre un ensemble de clusters de bases sur le regroupement d'un ensemble d'instances. En traitant ces motifs par groupes, en fonction du nombre de clusters de base qui définissent le motif, la méthode MultiCons génère une solution de consensus pour chaque groupe, générant par conséquence plusieurs consensus candidats. Ces différentes solutions sont ensuite représentées dans une structure arborescente appelée arbre de consensus, ouConsTree. Cette représentation graphique facilite la compréhension du processus de construction des multiples consensus, ainsi que les relations entre les instances et les structures d'instances dans l'espace de données.

Clustering is the process of partitioning a dataset into groups, so that the instances in the same group are more similar to each other than to instances in any other group. Many clustering algorithms were proposed, but none of them proved to provide good quality partition in all situations. Consensus clustering aims to enhance the clustering process by combining different partitions obtained from different algorithms to yield a better quality consensus solution. In this work, a new consensus clustering method, called MultiCons, is proposed. It uses the frequent closed itemset mining technique in order to discover the similarities between the different base clustering solutions. The identified similarities are presented in a form of clustering patterns, that each defines the agreement between a set of base clusters in grouping a set of instances. By dividing these patterns into groups based on the number of base clusters that define the pattern, MultiCons generates a consensussolution from each group, resulting in having multiple consensus candidates. These different solutions are presented in a tree-like structure, called ConsTree, that facilitates understanding the process of building the multiple consensuses, and also the relationships between the data instances and their structuring in the data space. Five consensus functions are proposed in this work in order to build a consensus solution from the clustering patterns. Approach 1 is to just merge any intersecting clustering patterns. Approach 2 can either merge or split intersecting patterns based on a proposed measure, called intersection ratio.

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Primary Entity<\/h3>\n
<http:\/\/www.worldcat.org\/oclc\/973925459<\/a>> # Une approche bas\u00E9e sur les motifs ferm\u00E9s pour r\u00E9soudre le probl\u00E8me de clustering par consensus<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:MediaObject<\/a>, schema:CreativeWork<\/a>, bgn:ComputerFile<\/a>, bgn:Thesis<\/a> ;\u00A0\u00A0\u00A0\nbgn:inSupportOf<\/a> \"Th\u00E8se de doctorat : Informatique : Universit\u00E9 C\u00F4te d\'Azur (ComUE) : 2016.<\/span>\" ;\u00A0\u00A0\u00A0\nlibrary:oclcnum<\/a> \"973925459<\/span>\" ;\u00A0\u00A0\u00A0\nlibrary:placeOfPublication<\/a> <http:\/\/id.loc.gov\/vocabulary\/countries\/fr<\/a>> ;\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Thing\/itemsets_frequents_fermes<\/a>> ; # Itemsets fr\u00E9quents ferm\u00E9s<\/span>\n\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Thing\/partitionnement_de_donnees_par_consensus<\/a>> ; # Partitionnement de donn\u00E9es par consensus<\/span>\n\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Thing\/ensembles_de_partitionnement_de_donnees<\/a>> ; # Ensembles de partitionnement de donn\u00E9es<\/span>\n\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Topic\/classification_automatique<\/a>> ; # Classification automatique<\/span>\n\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Thing\/partitionnement_de_donnees<\/a>> ; # Partitionnement de donn\u00E9es<\/span>\n\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Topic\/analyse_des_donnees<\/a>> ; # Analyse des donn\u00E9es<\/span>\n\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Thing\/classification_non_supervisee<\/a>> ; # Classification non-supervis\u00E9e<\/span>\n\u00A0\u00A0\u00A0\nschema:author<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/al_najdi_atheer<\/a>> ; # Atheer Al-Najdi<\/span>\n\u00A0\u00A0\u00A0\nschema:contributor<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/precioso_frederic_1974_professeur_des_universites<\/a>> ; # professeur des universit\u00E9s). Fr\u00E9d\u00E9ric Precioso<\/span>\n\u00A0\u00A0\u00A0\nschema:contributor<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/tettamanzi_andrea<\/a>> ; # Andrea Tettamanzi<\/span>\n\u00A0\u00A0\u00A0\nschema:contributor<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/bertet_karell_1972<\/a>> ; # Karell Bertet<\/span>\n\u00A0\u00A0\u00A0\nschema:contributor<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Organization\/ecole_doctorale_sciences_et_technologies_de_l_information_et_de_la_communication_sophia_antipolis_alpes_maritimes<\/a>> ; # \u00C9cole doctorale Sciences et technologies de l\'information et de la communication (Sophia Antipolis, Alpes-Maritimes).<\/span>\n\u00A0\u00A0\u00A0\nschema:contributor<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Organization\/laboratoire_informatique_signaux_et_systemes_sophia_antipolis_alpes_maritimes<\/a>> ; # Laboratoire Informatique, signaux et syst\u00E8mes (Sophia Antipolis, Alpes-Maritimes).<\/span>\n\u00A0\u00A0\u00A0\nschema:contributor<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Organization\/universite_de_nice_1965_2019<\/a>> ; # Universit\u00E9 de Nice (1965-2019).<\/span>\n\u00A0\u00A0\u00A0\nschema:contributor<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/fournier_viger_philippe<\/a>> ; # Philippe Fournier-Viger<\/span>\n\u00A0\u00A0\u00A0\nschema:contributor<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/pasquier_nicolas<\/a>> ; # Nicolas Pasquier<\/span>\n\u00A0\u00A0\u00A0\nschema:contributor<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Organization\/universite_cote_d_azur_2015_2019<\/a>> ; # Universit\u00E9 C\u00F4te d\'Azur (2015-2019).<\/span>\n\u00A0\u00A0\u00A0\nschema:contributor<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/revel_arnaud<\/a>> ; # Arnaud Revel<\/span>\n\u00A0\u00A0\u00A0\nschema:contributor<\/a> <http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/bandyopadhyay_sanghamitra_1968<\/a>> ; # Sanghamitra Bandyopadhyay<\/span>\n\u00A0\u00A0\u00A0\nschema:datePublished<\/a> \"2016<\/span>\" ;\u00A0\u00A0\u00A0\nschema:description<\/a> \"Clustering is the process of partitioning a dataset into groups, so that the instances in the same group are more similar to each other than to instances in any other group. Many clustering algorithms were proposed, but none of them proved to provide good quality partition in all situations. Consensus clustering aims to enhance the clustering process by combining different partitions obtained from different algorithms to yield a better quality consensus solution. In this work, a new consensus clustering method, called MultiCons, is proposed. It uses the frequent closed itemset mining technique in order to discover the similarities between the different base clustering solutions. The identified similarities are presented in a form of clustering patterns, that each defines the agreement between a set of base clusters in grouping a set of instances. By dividing these patterns into groups based on the number of base clusters that define the pattern, MultiCons generates a consensussolution from each group, resulting in having multiple consensus candidates. These different solutions are presented in a tree-like structure, called ConsTree, that facilitates understanding the process of building the multiple consensuses, and also the relationships between the data instances and their structuring in the data space. Five consensus functions are proposed in this work in order to build a consensus solution from the clustering patterns. Approach 1 is to just merge any intersecting clustering patterns. Approach 2 can either merge or split intersecting patterns based on a proposed measure, called intersection ratio.<\/span>\" ;\u00A0\u00A0\u00A0\nschema:description<\/a> \"Le clustering est le processus de partitionnement d\'un ensemble de donn\u00E9es en groupes, de sorte que les instances du m\u00EAme groupe sont plus semblables les unes aux autres qu\'avec celles de tout autre groupe. De nombreux algorithmes de clustering ont \u00E9t\u00E9 propos\u00E9s, mais aucun d\'entre eux ne s\'av\u00E8re fournir une partitiondes donn\u00E9es pertinente dans toutes les situations. Le clustering par consensus vise \u00E0 am\u00E9liorer le processus de regroupement en combinant diff\u00E9rentes partitions obtenues \u00E0 partir de divers algorithmes afin d\'obtenir une solution de consensus de meilleure qualit\u00E9. Dans ce travail, une nouvelle m\u00E9thode de clustering par consensus, appel\u00E9e MultiCons, est propos\u00E9e. Cette m\u00E9thode utilise la technique d\'extraction des itemsets fr\u00E9quents ferm\u00E9s dans le but de d\u00E9couvrir les similitudes entre les diff\u00E9rentes solutions de clustering dits de base. Les similitudes identifi\u00E9es sont repr\u00E9sent\u00E9es sous une forme de motifs de clustering, chacun d\u00E9finissant un accord entre un ensemble de clusters de bases sur le regroupement d\'un ensemble d\'instances. En traitant ces motifs par groupes, en fonction du nombre de clusters de base qui d\u00E9finissent le motif, la m\u00E9thode MultiCons g\u00E9n\u00E8re une solution de consensus pour chaque groupe, g\u00E9n\u00E9rant par cons\u00E9quence plusieurs consensus candidats. Ces diff\u00E9rentes solutions sont ensuite repr\u00E9sent\u00E9es dans une structure arborescente appel\u00E9e arbre de consensus, ouConsTree. Cette repr\u00E9sentation graphique facilite la compr\u00E9hension du processus de construction des multiples consensus, ainsi que les relations entre les instances et les structures d\'instances dans l\'espace de donn\u00E9es.<\/span>\" ;\u00A0\u00A0\u00A0\nschema:exampleOfWork<\/a> <http:\/\/worldcat.org\/entity\/work\/id\/4084836844<\/a>> ;\u00A0\u00A0\u00A0\nschema:genre<\/a> \"Th\u00E8ses et \u00E9crits acad\u00E9miques<\/span>\" ;\u00A0\u00A0\u00A0\nschema:inLanguage<\/a> \"en<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Une approche bas\u00E9e sur les motifs ferm\u00E9s pour r\u00E9soudre le probl\u00E8me de clustering par consensus<\/span>\" ;\u00A0\u00A0\u00A0\nschema:productID<\/a> \"973925459<\/span>\" ;\u00A0\u00A0\u00A0\nschema:url<\/a> <http:\/\/www.theses.fr\/2016AZUR4111\/document<\/a>> ;\u00A0\u00A0\u00A0\nschema:url<\/a> <https:\/\/tel.archives-ouvertes.fr\/tel-01478626<\/a>> ;\u00A0\u00A0\u00A0\nschema:url<\/a> <http:\/\/www.theses.fr\/2016AZUR4111\/abes<\/a>> ;\u00A0\u00A0\u00A0\nwdrs:describedby<\/a> <http:\/\/www.worldcat.org\/title\/-\/oclc\/973925459<\/a>> ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n\n

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<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Organization\/ecole_doctorale_sciences_et_technologies_de_l_information_et_de_la_communication_sophia_antipolis_alpes_maritimes<\/a>> # \u00C9cole doctorale Sciences et technologies de l\'information et de la communication (Sophia Antipolis, Alpes-Maritimes).<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Organization<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"\u00C9cole doctorale Sciences et technologies de l\'information et de la communication (Sophia Antipolis, Alpes-Maritimes).<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Organization\/laboratoire_informatique_signaux_et_systemes_sophia_antipolis_alpes_maritimes<\/a>> # Laboratoire Informatique, signaux et syst\u00E8mes (Sophia Antipolis, Alpes-Maritimes).<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Organization<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Laboratoire Informatique, signaux et syst\u00E8mes (Sophia Antipolis, Alpes-Maritimes).<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Organization\/universite_cote_d_azur_2015_2019<\/a>> # Universit\u00E9 C\u00F4te d\'Azur (2015-2019).<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Organization<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Universit\u00E9 C\u00F4te d\'Azur (2015-2019).<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Organization\/universite_de_nice_1965_2019<\/a>> # Universit\u00E9 de Nice (1965-2019).<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Organization<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Universit\u00E9 de Nice (1965-2019).<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/al_najdi_atheer<\/a>> # Atheer Al-Najdi<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Al-Najdi<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Atheer<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Atheer Al-Najdi<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/bandyopadhyay_sanghamitra_1968<\/a>> # Sanghamitra Bandyopadhyay<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:birthDate<\/a> \"1968<\/span>\" ;\u00A0\u00A0\u00A0\nschema:deathDate<\/a> \"\" ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Bandyopadhyay<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Sanghamitra<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Sanghamitra Bandyopadhyay<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/bertet_karell_1972<\/a>> # Karell Bertet<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:birthDate<\/a> \"1972<\/span>\" ;\u00A0\u00A0\u00A0\nschema:deathDate<\/a> \"\" ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Bertet<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Karell<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Karell Bertet<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/fournier_viger_philippe<\/a>> # Philippe Fournier-Viger<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Fournier-Viger<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Philippe<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Philippe Fournier-Viger<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/pasquier_nicolas<\/a>> # Nicolas Pasquier<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Pasquier<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Nicolas<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Nicolas Pasquier<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/precioso_frederic_1974_professeur_des_universites<\/a>> # professeur des universit\u00E9s). Fr\u00E9d\u00E9ric Precioso<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:birthDate<\/a> \"1974<\/span>\" ;\u00A0\u00A0\u00A0\nschema:deathDate<\/a> \";<\/span>\" ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Precioso<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Fr\u00E9d\u00E9ric<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"professeur des universit\u00E9s). Fr\u00E9d\u00E9ric Precioso<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/revel_arnaud<\/a>> # Arnaud Revel<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Revel<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Arnaud<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Arnaud Revel<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Person\/tettamanzi_andrea<\/a>> # Andrea Tettamanzi<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Person<\/a> ;\u00A0\u00A0\u00A0\nschema:familyName<\/a> \"Tettamanzi<\/span>\" ;\u00A0\u00A0\u00A0\nschema:givenName<\/a> \"Andrea<\/span>\" ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Andrea Tettamanzi<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Thing\/classification_non_supervisee<\/a>> # Classification non-supervis\u00E9e<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Thing<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Classification non-supervis\u00E9e<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Thing\/ensembles_de_partitionnement_de_donnees<\/a>> # Ensembles de partitionnement de donn\u00E9es<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Thing<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Ensembles de partitionnement de donn\u00E9es<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Thing\/itemsets_frequents_fermes<\/a>> # Itemsets fr\u00E9quents ferm\u00E9s<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Thing<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Itemsets fr\u00E9quents ferm\u00E9s<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Thing\/partitionnement_de_donnees<\/a>> # Partitionnement de donn\u00E9es<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Thing<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Partitionnement de donn\u00E9es<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Thing\/partitionnement_de_donnees_par_consensus<\/a>> # Partitionnement de donn\u00E9es par consensus<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Thing<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Partitionnement de donn\u00E9es par consensus<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Topic\/analyse_des_donnees<\/a>> # Analyse des donn\u00E9es<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Intangible<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Analyse des donn\u00E9es<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/experiment.worldcat.org\/entity\/work\/data\/4084836844#Topic\/classification_automatique<\/a>> # Classification automatique<\/span>\n\u00A0\u00A0\u00A0\u00A0a \nschema:Intangible<\/a> ;\u00A0\u00A0\u00A0\nschema:name<\/a> \"Classification automatique<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/id.loc.gov\/vocabulary\/countries\/fr<\/a>>\u00A0\u00A0\u00A0\u00A0a \nschema:Place<\/a> ;\u00A0\u00A0\u00A0\ndcterms:identifier<\/a> \"fr<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/www.theses.fr\/2016AZUR4111\/document<\/a>>\u00A0\u00A0\u00A0\nrdfs:comment<\/a> \"Acc\u00E8s au texte int\u00E9gral<\/span>\" ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n
<http:\/\/www.worldcat.org\/title\/-\/oclc\/973925459<\/a>>\u00A0\u00A0\u00A0\u00A0a \ngenont:InformationResource<\/a>, genont:ContentTypeGenericResource<\/a> ;\u00A0\u00A0\u00A0\nschema:about<\/a> <http:\/\/www.worldcat.org\/oclc\/973925459<\/a>> ; # Une approche bas\u00E9e sur les motifs ferm\u00E9s pour r\u00E9soudre le probl\u00E8me de clustering par consensus<\/span>\n\u00A0\u00A0\u00A0\nschema:dateModified<\/a> \"2020-10-05<\/span>\" ;\u00A0\u00A0\u00A0\nvoid:inDataset<\/a> <http:\/\/purl.oclc.org\/dataset\/WorldCat<\/a>> ;\u00A0\u00A0\u00A0\u00A0.\n\n\n<\/div>\n\n

Content-negotiable representations<\/p>\n