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Band 1574

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

Aus der Reihe Lecture Notes in Computer Science

49,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

14.04.1999

Abbildungen

XVI, 540 p.

Herausgeber

Ning Zhong + weitere

Verlag

Springer Berlin

Seitenzahl

540

Maße (L/B/H)

23,5/15,5/3 cm

Gewicht

832 g

Auflage

1999

Sprache

Englisch

ISBN

978-3-540-65866-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

14.04.1999

Abbildungen

XVI, 540 p.

Herausgeber

Verlag

Springer Berlin

Seitenzahl

540

Maße (L/B/H)

23,5/15,5/3 cm

Gewicht

832 g

Auflage

1999

Sprache

Englisch

ISBN

978-3-540-65866-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Methodologies for Knowledge Discovery and Data Mining
  • Produktbild: Methodologies for Knowledge Discovery and Data Mining
  • 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.