Knowledge discovery and data mining : current issues and new applications : 4th Pacific-Asia Conference, PAKDD 2000, Kyoto, Japan, April 18-20, 2000 : proceedings
The Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000) was held at the Keihanna-Plaza, Kyoto, Japan, April 18 - 20, 2000. PAKDD 2000 provided an international forum for researchers and applica tion developers to share their original research results and practical deve...
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| Andere auteurs: | , , |
| Formaat: | Livre numérique |
| Taal: | Anglais |
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Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Reeks: | Lecture notes in computer science. Lecture notes in artificial intelligence
1805 |
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| Online toegang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Knowledge discovery and data mining, current issues and new applications, 4th Pacific-Asia Conference, PAKDD 2000, Kyoto, Japan, April 18-20, 2000, proceedings, Takao Terano, Huan Liu, Arbee L.P. Chen (eds.), 2000, New York, Springer, 1 vol. (XIV-460 p.), Lecture notes in computer science, 3-540-67382-2 • Knowledge Discovery and Data Mining. Current Issues and New Applications, Texte imprimé, 9783662190692 |
Inhoudsopgave:
- Keynote Speeches and Invited Talk
- Perspective on Data Mining from Statistical Viewpoints
- Inductive Databases and Knowledge Scouts
- Hyperlink-Aware Mining and Analysis of the Web
- Data Mining Theory
- Polynomial Time Matching Algorithms for Tree-Like Structured Patterns in Knowledge Discovery
- Fast Discovery of Interesting Rules
- Performance Controlled Data Reduction for Knowledge Discovery in Distributed Databases
- Minimum Message Length Criterion for Second-Order Polynomial Model Discovery
- Frequent Itemset Counting Across Multiple Tables
- Frequent Closures as a Concise Representation for Binary Data Mining
- An Optimization Problem in Data Cube System Design
- Exception Rule Mining with a Relative Interestingness Measure
- Feature Selection and Transformation
- Consistency Based Feature Selection
- Feature Selection for Clustering
- A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases
- Missing Value Estimation Based on Dynamic Attribute Selection
- On Association, Similarity and Dependency of Attributes
- Clustering
- Prototype Generation Based on Instance Filtering and Averaging
- A Visual Method of Cluster Validation with Fastmap
- COE: Clustering with Obstacles Entities A Preliminary Study
- Combining Sampling Technique with DBSCAN Algorithm for Clustering Large Spatial Databases
- Predictive Adaptive Resonance Theory and Knowledge Discovery in Databases
- Improving Generalization Ability of Self-Generating Neural Networks Through Ensemble Averaging
- Application of Data Mining
- Attribute Transformations on Numerical Databases
- Efficient Detection of Local Interactions in the Cascade Model
- Extracting Predictors of Corporate Bankruptcy: Empirical Study on Data Mining Methods
- Evaluating Hypothesis-Driven Exception-Rule Discovery with Medical Data Sets
- Discovering Protein Functional Models Using Inductive Logic Programming
- Mining Web Transaction Patterns in an Electronic Commerce Environment
- Association Rules and Related Topics
- Making Use of the Most Expressive Jumping Emerging Patterns for Classification
- Mining Structured Association Patterns from Databases
- Association Rules
- Density-Based Mining of Quantitative Association Rules
- AViz: A Visualization System for Discovering Numeric Association Rules
- Discovering Unordered and Ordered Phrase Association Patterns for Text Mining
- Using Random Walks for Mining Web Document Associations
- Induction
- A Concurrent Approach to the Key-Preserving Attribute-Oriented Induction Method
- Scaling Up a Boosting-Based Learner via Adaptive Sampling
- Adaptive Boosting for Spatial Functions with Unstable Driving Attributes
- Robust Ensemble Learning for Data Mining
- Interactive Visualization in Mining Large Decision Trees
- VQTree: Vector Quantization for Decision Tree Induction
- Making Knowledge Extraction and Reasoning Closer
- Discovery of Relevant Weights by Minimizing Cross-Validation Error
- Efficient and Comprehensible Local Regression
- Information Granules for Spatial Reasoning
- Text, Web, and Graph Mining
- Uncovering the Hierarchical Structure of Text Archives by Using an Unsupervised Neural Network with Adaptive Architecture
- Mining Access Patterns Efficiently from Web Logs
- A Comparative Study of Classification Based Personal E-mail Filtering
- Extension of Graph-Based Induction for General Graph Structured Data
- Text-Source Discovery and GlOSS Update in a Dynamic Web
- Extraction of Fuzzy Clusters from Weighted Graphs
- Text Summarization by Sentence Segment Extraction Using Machine Learning Algorithms.