Methodologies for knowledge discovery and data mining : Third Pacific-Asia Conference, PAKDD-99, Beijing, China, April 26 28, 1999 : proceedings
Salvato in:
| Ente Autore: | |
|---|---|
| Altri autori: | , |
| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Serie: | Lecture notes in computer science. Lecture notes in artificial intelligence
1574 |
| Soggetti: | |
| Accesso online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Methodologies for knowledge discovery and data mining, proceedings, Third Pacific-Asia Conference, PAKDD-99, Beijing, China, April 1999, 1999, Berlin, Springer, 1 vol. (XVI-424 p.), Lecture notes in computer science, 3-540-65866-1 • Methodologies for Knowledge Discovery and Data Mining, Texte imprimé, 9783662171127 |
Sommario:
- Invited Talks
- KDD as an Enterprise IT Tool: Reality and Agenda
- Computer Assisted Discovery of First Principle Equations from Numeric Data
- Emerging KDD Technology
- Data Mining a Rough Set Perspective
- Data Mining Techniques for Associations, Clustering and Classification
- Data Mining: Granular Computing Approach
- Rule Extraction from Prediction Models
- Association Rules
- Mining Association Rules on Related Numeric Attributes
- LGen A Lattice-Based Candidate Set Generation Algorithm for I/O Efficient Association Rule Mining
- Extending the Applicability of Association Rules
- An Efficient Approach for Incremental Association Rule Mining
- Association Rules in Incomplete Databases
- Parallel SQL Based Association Rule Mining on Large Scale PC Cluster: Performance Comparison with Directly Coded C Implementation
- H-Rule Mining in Heterogeneous Databases
- An Improved Definition of Multidimensional Inter-transaction Association Rule
- Incremental Discovering Association Rules: A Concept Lattice Approach
- Feature Selection and Generation
- Induction as Pre-processing
- Stochastic Attribute Selection Committees with Multiple Boosting: Learning More Accurate and More Stable Classifier Committees
- On Information-Theoretic Measures of Attribute Importance
- A Technique of Dynamic Feature Selection Using the Feature Group Mutual Information
- A Data Pre-processing Method Using Association Rules of Attributes for Improving Decision Tree
- Mining in Semi, Un-structured Data
- An Algorithm for Constrained Association Rule Mining in Semi-structured Data
- Incremental Mining of Schema for Semistructured Data
- Discovering Structure from Document Databases
- Combining Forecasts from Multiple Textual Data Sources
- Domain Knowledge Extracting in a Chinese NaturalLanguage Interface to Databases: NChiql
- Interestingness, Surprisingness, and Exceptions
- Evolutionary Hot Spots Data Mining
- Efficient Search of Reliable Exceptions
- Heuristics for Ranking the Interestingness of Discovered Knowledge
- Rough Sets, Fuzzy Logic, and Neural Networks
- Automated Discovery of Plausible Rules Based on Rough Sets and Rough Inclusion
- Discernibility System in Rough Sets
- Automatic Labeling of Self-Organizing Maps: Making a Treasure-Map Reveal Its Secrets
- Neural Network Based Classifiers for a Vast Amount of Data
- Accuracy Tuning on Combinatorial Neural Model
- A Situated Information Articulation Neural Network: VSF Network
- Neural Method for Detection of Complex Patterns in Databases
- Preserve Discovered Linguistic Patterns Valid in Volatility Data Environment
- An Induction Algorithm Based on Fuzzy Logic Programming
- Rule Discovery in Databases with Missing Values Based on Rough Set Model
- Sustainability Knowledge Mining from Human Development Database
- Induction, Classification, and Clustering
- Characterization of Default Knowledge in Ripple Down Rules Method
- Improving the Performance of Boosting for Naive Bayesian Classification
- Convex Hulls in Concept Induction
- Mining Classification Knowledge Based on Cloud Models
- Robust Clusterin of Large Geo-referenced Data Sets
- A Fast Algorithm for Density-Based Clustering in Large Database
- A Lazy Model-Based Algorithm for On-Line Classification
- An Efficient Space-Partitioning Based Algorithm for the K-Means Clustering
- A Fast Clustering Process for Outliers and Remainder Clusters
- Optimising the Distance Metric in the Nearest Neighbour Algorithm on a Real-World Patient Classification Problem
- Classifying Unseen Cases with Many Missing Values
- Study of a Mixed SimilarityMeasure for Classification and Clustering
- Visualization
- Visually Aided Exploration of Interesting Association Rules
- DVIZ: A System for Visualizing Data Mining
- Causal Model and Graph-Based Methods
- A Minimal Causal Model Learner
- Efficient Graph-Based Algorithm for Discovering and Maintaining Knowledge in Large Databases
- Basket Analysis for Graph Structured Data
- The Evolution of Causal Models: A Comparison of Bayesian Metrics and Structure Priors
- KD-FGS: A Knowledge Discovery System from Graph Data Using Formal Graph System
- Agent-Based, and Distributed Data Mining
- Probing Knowledge in Distributed Data Mining
- Discovery of Equations and the Shared Operational Semantics in Distributed Autonomous Databases
- The Data-Mining and the Technology of Agents to Fight the Illicit Electronic Messages
- Knowledge Discovery in SportsFinder: An Agent to Extract Sports Results from the Web
- Event Mining with Event Processing Networks
- Advanced Topics and New Methodologies
- An Analysis of Quantitative Measures Associated with Rules
- A Strong Relevant Logic Model of Epistemic Processes in Scientific Discovery
- Discovering Conceptual Differences among Different People via Diverse Structures
- Ordered Estimation of Missing Values
- Prediction Rule Discovery Based on Dynamic Bias Selection
- Discretization of Continuous Attributes for Learning Classification Rules
- BRRA: A Based Relevant Rectangles Algorithm for Mining Relationships in Databases
- Mining Functional Dependency Rule of Relational Database
- Time-Series Prediction with Cloud Models in DMKD.