Principles of data mining and knowledge discovery : 6th European Conference, PKDD 2002, Helsinki, Finland, August 19 23, 2002 : proceedings
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| מחברים אחרים: | , , |
| פורמט: | Livre numérique |
| שפה: | Anglais |
| יצא לאור: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| סדרה: | Lecture notes in computer science. Lecture notes in artificial intelligence
2431 |
| נושאים: | |
| גישה מקוונת: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| הערה: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Principles of Data Mining and Knowledge Discovery, 6th European Conference, PKDD 2002, Helsinki, Finland, August 19-23, 2002, Proceedings, Tapio Elomaa, Heikki Mannila, Hannu Toivonen (eds.), Berlin, Springer, 2002, 1 vol. (XIV-514 p.), Lecture notes in computer science, 3-540-44037-2 • Principles of Data Mining and Knowledge Discovery, Texte imprimé, 9783662209820 |
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| 009 | PPN15516709X | ||
| 020 | |a 9783540456810 (PDF) | ||
| 041 | 0 | |a eng | |
| 082 | |a 004 | ||
| 111 | 2 | |a European Conference on Principles and Practice of Knowledge Discovery in Databases |n (06 |d :2002 |c :Helsinki). | |
| 245 | 1 | 0 | |a Principles of data mining and knowledge discovery : |b 6th European Conference, PKDD 2002, Helsinki, Finland, August 19 23, 2002 : proceedings |c [edited by] Tapio Elomaa, Heikki Mannila, Hannu Toivonen. |
| 260 | |a Berlin [etc.] : |b Springer. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Lecture notes in computer science. Lecture notes in artificial intelligence |v 2431 |x 1611-3349 |x 2945-9141 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 0 | |a Contributed Papers -- Optimized Substructure Discovery for Semi-structured Data -- Fast Outlier Detection in High Dimensional Spaces -- Data Mining in Schizophrenia Research Preliminary Analysis -- Fast Algorithms for Mining Emerging Patterns -- On the Discovery of Weak Periodicities in Large Time Series -- The Need for Low Bias Algorithms in Classification Learning from Large Data Sets -- Mining All Non-derivable Frequent Itemsets -- Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance -- Finding Association Rules with Some Very Frequent Attributes -- Unsupervised Learning: Self-aggregation in Scaled Principal Component Space* -- A Classification Approach for Prediction of Target Events in Temporal Sequences -- Privacy-Oriented Data Mining by Proof Checking -- Choose Your Words Carefully: An Empirical Study of Feature Selection Metrics for Text Classification -- Generating Actionable Knowledge by Expert-Guided Subgroup Discovery -- Clustering Transactional Data -- Multiscale Comparison of Temporal Patterns in Time-Series Medical Databases -- Association Rules for Expressing Gradual Dependencies -- Support Approximations Using Bonferroni-Type Inequalities -- Using Condensed Representations for Interactive Association Rule Mining -- Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting -- Dependency Detection in MobiMine and Random Matrices -- Long-Term Learning for Web Search Engines -- Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database -- Involving Aggregate Functions in Multi-relational Search -- Information Extraction in Structured Documents Using Tree Automata Induction -- Algebraic Techniques for Analysis of Large Discrete-Valued Datasets -- Geography of Di.erences between Two Classes of Data -- Rule Induction for Classification of Gene Expression Array Data -- Clustering Ontology-Based Metadata in the Semantic Web -- Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases -- SVMClassification Using Sequences of Phonemes and Syllables -- A Novel Web Text Mining Method Using the Discrete Cosine Transform -- A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases -- Answering the Most Correlated N Association Rules Efficiently -- Mining Hierarchical Decision Rules from Clinical Databases Using Rough Sets and Medical Diagnostic Model -- Efficiently Mining Approximate Models of Associations in Evolving Databases -- Explaining Predictions from a Neural Network Ensemble One at a Time -- Structuring Domain-Specific Text Archives by Deriving a Probabilistic XML DTD -- Separability Index in Supervised Learning -- Invited Papers -- Finding Hidden Factors Using Independent Component Analysis -- Reasoning with Classifiers* -- A Kernel Approach for Learning from Almost Orthogonal Patterns -- Learning with Mixture Models: Concepts and Applications. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 650 | |a Informatique | ||
| 650 | |a Intelligence artificielle | ||
| 650 | |a Exploration de données | ||
| 650 | |a Logique symbolique et mathématique | ||
| 650 | |a Bases de données |x Interrogation | ||
| 650 | |a Informatique documentaire | ||
| 650 | |a Actes de congrès | ||
| 700 | 1 | |a Elomaa, Tapio. |4 pbd | |
| 700 | 1 | |a Mannila, Heikki, |d 1960- |4 pbd | |
| 700 | 1 | |a Toivonen, Hannu. |4 pbd | |
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| 776 | 0 | |t Principles of Data Mining and Knowledge Discovery |b Texte imprimé |z 9783662209820 | |
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| 856 | 4 | |5 180339901:754026140 |u https://ezproxy.insa-cvl.fr/login?qurl=https://doi.org/10.1007/3-540-45681-3 |z Accès INSA CVL | |
| 997 | |0 969076 |1 Livre numérique |a Ressource numérique |b INSA |b ENSA |c 0/Bibliothèque numérique/ |c 1/Bibliothèque numérique/Autre ressource numérique/ | ||