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accueil accueil présentation recherche annuaire intranet recevez notre newsletter : | présentation le laboratoire eric est une unité de recherche (équipe d'accueil 3083) multitutelle (universités lyon 1 et lyon 2 ) membre de la msh lyon st-étienne et rattachée à l' école doctorale infomaths et partenaire du labex imu , du gdr madics et du gis institut du genre . ses recherches se situent dans les domaines de la science des données et de l'informatique décisionnelle (entrepôts et fouille de données, apprentissage automatique, aide à la décision). en savoir plus... tweets by eric_ea3083 le laboratoire eric recrute ! poste de professeur·e des universités, 27e section (informatique) - profil : fouille de données, apprentissage automatique, big data, humanités numériques ( fiche de poste ) offre de thèse : des lacs de données à l’analyse de documents textuels (financement : région aura) -- tous les congrès/workshops congrès/workshop(s) 1 2 3 advances on big data management, analytics, data privacy and security ( le dim. 2 septembre 2018) 1 2 3 eda 2018 - business intelligence & big data (du jeu. 4 octobre 2018 au sam. 6 octobre 2018) 1 2 3 10e colloque francophone sur les sondages (du mer. 24 octobre 2018 au ven. 26 octobre 2018) tous les séminaires séminaire(s) lun. 17/09/2018 11:00 k71, bâtiment k, rdc acar hayri (assp - université lumière lyon 2) improve the quality of source code using teec sommaire: icts (information and communication technologies) are responsible around 2% of worldwide greenhouse gas emissions (gartner, 2007). and according to the intergovernmental panel on climate change (ippc) recent reports, co2 emissions due to icts are increasing widely. for this reason, many works tried to propose various tools to estimate the energy consumption due to software in order to reduce carbon footprint. however, these studies, in the majority of cases, takes into account only the cpu and neglects all others components. teec (tool to estimate energy consumption) is a tool able to estimate the power consumed by cpu, memory, disk and network due to the execution of an application at runtime. the main goal is to guide developers to improve their source code for optimizing energy consumption. teec enables determining the part of the code consuming the highest power. this will help to obtain a less energy consuming software with the same functionalities. pour plus d'informations, merci de contacter cugliari j. lun. 24/09/2018 11:00 k71, bâtiment k, rdc hernandez nicolas (department of statistics, university carlos iii de madrid) pour plus d'informations, merci de contacter cugliari j. publications référencées sur hal toutes les productions publications détection des intrusions et aide à la décision auteur(s): pierrot d. , harbi n. , darmont j. conference: 12e conférence sur les avancées des systèmes décisionnels (asd 2018) (marrakech, ma, 2018-05-02) actes de conférence: icps , vol. p. () ref hal: hal-01761914_v1 résumé: les conséquences d'une intrusion dans un système d'information peuvent s'avéver problématiques pour l'existence d'une entreprise ou d'une organisation. les impacts sont synonymes d'une perte financière, d'image de marque et de sérieux. la détection d'une intrusion n'est pas une finalité en soit, la ré-duction du delta détection-réaction est devenue prioritaire. nous proposons une méthode prenant en compte les aspects techniques par l'utilisation d'une mé-thode hybride de data mining mais aussi les aspects fonctionnels. l'addition de ces deux aspects permet d'obtenir une vision générale sur l'hygiène du système d'information mais aussi une orientation sur la surveillance et les corrections à apporter. maxmin linear initialization for fuzzy c-means auteur(s): oztürk a. , lallich s. , darmont j. , waksman sylvie yona conference: 14th international conference on machine learning and data mining (mldm 2018) (new york, us, 2018-07-14) actes de conférence: , vol. p. (2018) ref hal: hal-01771204_v1 résumé: clustering is an extensive research area in data science. the aim of clustering is to discover groups and to identify interesting patterns in datasets. crisp (hard) clustering considers that each data point belongs to one and only one cluster. however, it is inadequate as some data points may belong to several clusters, as is the case in text categorization. thus, we need more flexible clustering. fuzzy clustering methods, where each data point can belong to several clusters, are an interesting alternative. yet, seeding iterative fuzzy algorithms to achieve high quality clustering is an issue. in this paper, we propose a new linear and efficient initialization algorithm maxmin linear to deal with this problem. then, we validate our theoretical results through extensive experiments on a variety of numerical real-world and artificial datasets. we also test several validity indices, including a new validity index that we propose, transformed standardized fuzzy difference (tsfd). benchmarking top-k keyword and top-k document processing with t2k2 and t2k2d2 auteur(s): truica c.-o. , darmont j. , boicea alexandru, radulescu florin (article) publié: future generation computer systems , vol. p. (2018) ref hal: hal-01717121_v1 5, avenue pierre mendès france 69676 bron cedex, france tél. +33 (0)4 78 77 31 54 fax. +33 (0)4 78 77 23 75 webmaster | plan du site
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NSERVER
DIOGENE.UNIV-LYON2.FR 159.84.44.31
LAUREL.UNIV-LYON2.FR 159.84.64.49
DNS.UNIV-LYON1.FR 134.214.100.6
NAME univ-lyon2.fr
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