LEADER 01034nam0-2200373---450- 001 990010031340403321 005 20160316092730.0 035 $a001003134 035 $aFED01001003134 035 $a(Aleph)001003134FED01 035 $a001003134 100 $a20160122d1973----km-y0itay50------ba 101 0 $afre 102 $aFR 105 $ay-------001yy 200 1 $aÉducation et psychanalyse$fRobert Barande, Bruno Bettelheim, Julien Bigras... [et al.] 210 $aParis$cHachette$d1973 215 $a187 p.$d23 cm 225 1 $aInterprétation 610 0 $aPsicanalisi e educazione 610 0 $aEducazione 610 0 $aPsicoanalisi 676 $a616.891 676 $a150.195 702 1$aBarande,$bRobert 702 1$aBettelheim,$bBruno$f<1903-1990> 702 1$aBigras,$bJulien 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990010031340403321 952 $aP.1 PSI 1004$bBibl. 2015/703$fFLFBC 959 $aFLFBC 996 $aÉducation et psychanalyse$91499358 997 $aUNINA LEADER 07589nam 2200601 450 001 996465510503316 005 20210313005213.0 010 $a3-540-73871-1 024 7 $a10.1007/978-3-540-73871-8 035 $a(CKB)1000000000490209 035 $a(SSID)ssj0000315718 035 $a(PQKBManifestationID)11212740 035 $a(PQKBTitleCode)TC0000315718 035 $a(PQKBWorkID)10255226 035 $a(PQKB)10502823 035 $a(DE-He213)978-3-540-73871-8 035 $a(MiAaPQ)EBC3063397 035 $a(MiAaPQ)EBC6413195 035 $a(PPN)123164036 035 $a(EXLCZ)991000000000490209 100 $a20210313d2007 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 00$aAdvanced data mining and applications $eThird international conference, ADMA 2007, Harbin, China, August 6-8, 2007 : proceedings /$fReda Alhajj [and four others] 205 $a1st ed. 2007. 210 1$aBerlin, Germany ;$aNew York, New York :$cSpringer,$d[2007] 210 4$d?2007 215 $a1 online resource (XVI, 636 p. 201 illus.) 225 1 $aLecture Notes in Artificial Intelligence ;$v4632 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-73870-3 320 $aIncludes bibliographical references and index. 327 $aInvited Talk -- Mining Ambiguous Data with Multi-instance Multi-label Representation -- Regular Papers -- DELAY: A Lazy Approach for Mining Frequent Patterns over High Speed Data Streams -- Exploring Content and Linkage Structures for Searching Relevant Web Pages -- CLBCRA-Approach for Combination of Content-Based and Link-Based Ranking in Web Search -- Rough Sets in Hybrid Soft Computing Systems -- Discovering Novel Multistage Attack Strategies -- Privacy Preserving DBSCAN Algorithm for Clustering -- A New Multi-level Algorithm Based on Particle Swarm Optimization for Bisecting Graph -- A Supervised Subspace Learning Algorithm: Supervised Neighborhood Preserving Embedding -- A k-Anonymity Clustering Method for Effective Data Privacy Preservation -- LSSVM with Fuzzy Pre-processing Model Based Aero Engine Data Mining Technology -- A Coding Hierarchy Computing Based Clustering Algorithm -- Mining Both Positive and Negative Association Rules from Frequent and Infrequent Itemsets -- Survey of Improving Naive Bayes for Classification -- Privacy Preserving BIRCH Algorithm for Clustering over Arbitrarily Partitioned Databases -- Unsupervised Outlier Detection in Sensor Networks Using Aggregation Tree -- Separator: Sifting Hierarchical Heavy Hitters Accurately from Data Streams -- Spatial Fuzzy Clustering Using Varying Coefficients -- Collaborative Target Classification for Image Recognition in Wireless Sensor Networks -- Dimensionality Reduction for Mass Spectrometry Data -- The Study of Dynamic Aggregation of Relational Attributes on Relational Data Mining -- Learning Optimal Kernel from Distance Metric in Twin Kernel Embedding for Dimensionality Reduction and Visualization of Fingerprints -- Efficiently Monitoring Nearest Neighbors to a Moving Object -- A Novel Text Classification Approach Based on Enhanced Association Rule -- Applications of the Moving Average of n th -Order Difference Algorithm for Time Series Prediction -- Inference of Gene Regulatory Network by Bayesian Network Using Metropolis-Hastings Algorithm -- A Consensus Recommender for Web Users -- Constructing Classification Rules Based on SVR and Its Derivative Characteristics -- Hiding Sensitive Associative Classification Rule by Data Reduction -- AOG-ags Algorithms and Applications -- A Framework for Titled Document Categorization with Modified Multinomial Naivebayes Classifier -- Prediction of Protein Subcellular Locations by Combining K-Local Hyperplane Distance Nearest Neighbor -- A Similarity Retrieval Method in Brain Image Sequence Database -- A Criterion for Learning the Data-Dependent Kernel for Classification -- Topic Extraction with AGAPE -- Clustering Massive Text Data Streams by Semantic Smoothing Model -- GraSeq: A Novel Approximate Mining Approach of Sequential Patterns over Data Stream -- A Novel Greedy Bayesian Network Structure Learning Algorithm for Limited Data -- Optimum Neural Network Construction Via Linear Programming Minimum Sphere Set Covering -- How Investigative Data Mining Can Help Intelligence Agencies to Discover Dependence of Nodes in Terrorist Networks -- Prediction of Enzyme Class by Using Reactive Motifs Generated from Binding and Catalytic Sites -- Bayesian Network Structure Ensemble Learning -- Fusion of Palmprint and Iris for Personal Authentication -- Enhanced Graph Based Genealogical Record Linkage -- A Fuzzy Comprehensive Clustering Method -- Short Papers -- CACS: A Novel Classification Algorithm Based on Concept Similarity -- Data Mining in Tourism Demand Analysis: A Retrospective Analysis -- Chinese Patent Mining Based on Sememe Statistics and Key-Phrase Extraction -- Classification of Business Travelers Using SVMs Combined with Kernel Principal Component Analysis -- Research on the Traffic Matrix Based on Sampling Model -- A Causal Analysis for the Expenditure Data of Business Travelers -- A Visual and Interactive Data Exploration Method for Large Data Sets and Clustering -- Explorative Data Mining on Stock Data ? Experimental Results and Findings -- Graph Structural Mining in Terrorist Networks -- Characterizing Pseudobase and Predicting RNA Secondary Structure with Simple H-Type Pseudoknots Based on Dynamic Programming -- Locally Discriminant Projection with Kernels for Feature Extraction -- A GA-Based Feature Subset Selection and Parameter Optimization of Support Vector Machine for Content ? Based Image Retrieval -- E-Stream: Evolution-Based Technique for Stream Clustering -- H-BayesClust: A New Hierarchical Clustering Based on Bayesian Networks -- An Improved AdaBoost Algorithm Based on Adaptive Weight Adjusting. 330 $aThe Third International Conference on Advanced Data Mining and Applications (ADMA) organized in Harbin, China continued the tradition already established by the first two ADMA conferences in Wuhan in 2005 and Xi?an in 2006. One major goal of ADMA is to create a respectable identity in the data mining research com- nity. This feat has been partially achieved in a very short time despite the young age of the conference, thanks to the rigorous review process insisted upon, the outstanding list of internationally renowned keynote speakers and the excellent program each year. The impact of a conference is measured by the citations the conference papers receive. Some have used this measure to rank conferences. For example, the independent source cs-conference-ranking.org ranks ADMA (0.65) higher than PAKDD (0.64) and PKDD (0.62) as of June 2007, which are well established conferences in data mining. While the ranking itself is questionable because the exact procedure is not disclosed, it is nevertheless an encouraging indicator of recognition for a very young conference such as ADMA. 410 0$aLecture Notes in Artificial Intelligence ;$v4632 606 $aComputer science 606 $aArtificial intelligence 606 $aData mining 615 0$aComputer science. 615 0$aArtificial intelligence. 615 0$aData mining. 676 $a005.74 702 $aAlhajj$b Reda 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465510503316 996 $aAdvanced Data Mining and Applications$9771989 997 $aUNISA LEADER 03096 am 2200661 n 450 001 9910576800103321 005 20220524 010 $a2-940655-18-9 024 7 $a10.4000/books.eie.1210 035 $a(CKB)4100000012877756 035 $a(FrMaCLE)OB-eie-1210 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/85241 035 $a(PPN)263750973 035 $a(EXLCZ)994100000012877756 100 $a20220621j|||||||| ||| 0 101 0 $afre 135 $auu||||||m|||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComment susciter la motivation des élèves pour la grammaire ? $eRéflexions autour d?une séquence didactique /$fSarah Gremion 210 $aGenève $cÉditions Interroger l?éducation$d2022 215 $a1 online resource (158 p.) 225 1 $aCahiers de la Section des sciences de l?éducation 311 $a2-940195-99-4 330 $aCe livre propose une réflexion sur l?enseignement d?un sujet réputé difficile et impopulaire : la grammaire. Comment motiver les élèves en grammaire et comment mettre en ?uvre des démarches d?enseignement engageantes ? Pour tester, dans sa pratique, des outils pour susciter la motivation des élèves, Sarah Gremion travaille avec des élèves de 10?11 ans sur la notion d?attribut du sujet. Le livre propose une description précise des démarches d?enseignement mises en place, à partir desquelles tout enseignant ou stagiaire pourra se projeter dans son propre contexte de classe. Optant pour un enseignement de la grammaire dosant induction et déduction, l?autrice encourage à expérimenter et à s?adapter, se documenter et oser. De ce travail de terrain, l?autrice dégage cinq principes qui aideront les nouveaux enseignants à planifier des séquences didactiques en grammaire ; et qui aideront les élèves à entrer dans l?apprentissage, pour comprendre à quoi sert la grammaire. 517 $aComment susciter la motivation des élèves pour la grammaire ? 517 $aComment susciter la motivation des Ãlèves pour la grammaire ? 606 $aEducation & Educational Research 606 $agrammaire 606 $amotivation 606 $asens du travail scolaire 606 $adidactique 606 $ainduction 606 $agrammar 606 $aschoolwork 606 $adidactics 610 $agrammar 610 $amotivation 610 $aschoolwork 610 $adidactics 610 $ainduction 615 4$aEducation & Educational Research 615 4$agrammaire 615 4$amotivation 615 4$asens du travail scolaire 615 4$adidactique 615 4$ainduction 615 4$agrammar 615 4$aschoolwork 615 4$adidactics 700 $aGremion$b Sarah$01300680 701 $aBulea Bronckart$b Ecaterina$01300681 801 0$bFR-FrMaCLE 906 $aBOOK 912 $a9910576800103321 996 $aComment susciter la motivation des élèves pour la grammaire$93025629 997 $aUNINA