Methodologies for Knowledge Discovery and Data Mining Third Pacific-Asia Conference, PAKDD'99, Beijing, China, April 26-28, 1999, Proceedings /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Zhong, Ning (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Zhou, Lizhu (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1999.
• Έκδοση: 1st ed. 1999.
• Σειρά: Lecture Notes in Artificial Intelligence ; 1574
• Θέματα: Artificial intelligence.; Database management.; Information storage and retrieval.; Application software.; Information technology.; Business-Data processing.; Artificial Intelligence.; Database Management.; Information Storage and Retrieval.; Information Systems Applications (incl. Internet).; IT in Business.

 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 Natural Language 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 Similarity Measure 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.