Vor Ort
Merkzettel Warenkorb
Produktbild: Principles of Data Mining and Knowledge Discovery

Principles of Data Mining and Knowledge Discovery Third European Conference, PKDD '99, Prague, Czech Republic, September 15-18, 1999. Proceedings

Aus der Reihe Lecture Notes in Computer Science

99,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.09.1999

Herausgeber

Jan Zytkow + weitere

Verlag

Springer Berlin

Seitenzahl

593

Maße (L/B/H)

23,5/15,5/3,2 cm

Gewicht

920 g

Auflage

1999

Sprache

Englisch

ISBN

978-3-540-66490-1

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

01.09.1999

Herausgeber

Verlag

Springer Berlin

Seitenzahl

593

Maße (L/B/H)

23,5/15,5/3,2 cm

Gewicht

920 g

Auflage

1999

Sprache

Englisch

ISBN

978-3-540-66490-1

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Principles of Data Mining and Knowledge Discovery
  • Session 1A - Time Series.- Scaling up Dynamic Time Warping to Massive Datasets.- The Haar Wavelet Transform in the Time Series Similarity Paradigm.- Rule Discovery in Large Time-Series Medical Databases.- Session 1B - Applications.- Simultaneous Prediction of Multiple Chemical Parameters of River Water Quality with TILDE.- Applying Data Mining Techniques to Wafer Manufacturing.- An Application of Data Mining to the Problem of the University Students’ Dropout Using Markov Chains.- Session 2A - Taxonomies and Partitions.- Discovering and Visualizing Attribute Associations Using Bayesian Networks and Their Use in KDD.- Taxonomy Formation by Approximate Equivalence Relations, Revisited.- On the Use of Self-Organizing Maps for Clustering and Visualization.- Speeding Up the Search for Optimal Partitions.- Session 2B - Logic Methods.- Experiments in Meta-level Learning with ILP.- Boolean Reasoning Scheme with Some Applications in Data Mining.- On the Correspondence between Classes of Implicational and Equivalence Quantifiers.- Querying Inductive Databases via Logic-Based User-Defined Aggregates.- Session 3A - Distributed and Multirelational Databases.- Peculiarity Oriented Multi-database Mining.- Knowledge Discovery in Medical Multi-databases: A Rough Set Approach.- Automated Discovery of Rules and Exceptions from Distributed Databases Using Aggregates.- Session 3B - Text Mining and Feature Selection.- Text Mining via Information Extraction.- TopCat: Data Mining for Topic Identification in a Text Corpus.- Selection and Statistical Validation of Features and Prototypes.- Session 4A - Rules and Induction.- Taming Large Rule Models in Rough Set Approaches.- Optimizing Disjunctive Association Rules.- Contribution of Boosting in Wrapper Models.- Experiments on a Representation-Independent “Top-Down and Prune” Induction Scheme.- Session 5A - Interesting and Unusual.- Heuristic Measures of Interestingness.- Enhancing Rule Interestingness for Neuro-fuzzy Systems.- Unsupervised Profiling for Identifying Superimposed Fraud.- OPTICS-OF: Identifying Local Outliers.- Posters.- Selective Propositionalization for Relational Learning.- Circle Graphs: New Visualization Tools for Text-Mining.- On the Consistency of Information Filters for Lazy Learning Algorithms.- Using Genetic Algorithms to Evolve a Rule Hierarchy.- Mining Temporal Features in Association Rules.- The Improvement of Response Modeling: Combining Rule-Induction and Case-Based Reasoning.- Analyzing an Email Collection Using Formal Concept Analysis.- Business Focused Evaluation Methods: A Case Study.- Combining Data and Knowledge by MaxEnt-Optimization of Probability Distributions.- Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation?.- Rough Dependencies as a Particular Case of Correlation: Application to the Calculation of Approximative Reducts.- A Fuzzy Beam-Search Rule Induction Algorithm.- An Innovative GA-Based Decision Tree Classifier in Large Scale Data Mining.- Extension to C-means Algorithm for the Use of Similarity Functions.- Predicting Chemical Carcinogenesis Using Structural Information Only.- LA – A Clustering Algorithm with an Automated Selection of Attributes, Which is Invariant to Functional Transformations of Coordinates.- Association Rule Selection in a Data Mining Environment.- Multi-relational Decision Tree Induction.- Learning of Simple Conceptual Graphs from Positive and Negative Examples.- An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction.- ZigZag, a New Clustering Algorithm to Analyze Categorical Variable Cross-Classification Tables.- Efficient Mining of High Confidence Association Rules without Support Thresholds.- A Logical Approach to Fuzzy Data Analysis.- AST: Support for Algorithm Selection with a CBR Approach.- Efficient Shared Near Neighbours Clustering of Large Metric Data Sets.- Discovery of “Interesting” Data Dependencies from a Workload of SQL Statements.- Learning from Highly Structured Data by Decomposition.- Combinatorial Approach for Data Binarization.- Extending Attribute-Oriented Induction as a Key-Preserving Data Mining Method.- Automated Discovery of Polynomials by Inductive Genetic Programming.- Diagnosing Acute Appendicitis with Very Simple Classification Rules.- Rule Induction in Cascade Model Based on Sum of Squares Decomposition.- Maintenance of Discovered Knowledge.- A Divisive Initialisation Method for Clustering Algorithms.- A Comparison of Model Selection Procedures for Predicting Turning Points in Financial Time Series.- Mining Lemma Disambiguation Rules from Czech Corpora.- Adding Temporal Semantics to Association Rules.- Studying the Behavior of Generalized Entropy in Induction Trees Using a M-of-N Concept.- Discovering Rules in Information Trees.- Mining Text Archives: Creating Readable Maps to Structure and Describe Document Collections.- Neuro-fuzzy Data Mining for Target Group Selection in Retail Banking.- Mining Possibilistic Set-Valued Rules by Generating Prime Disjunctions.- Towards Discovery of Information Granules.- Classification Algorithms Based on Linear Combinations of Features.- Managing Interesting Rules in Sequence Mining.- Support Vector Machines for Knowledge Discovery.- Regression by Feature Projections.- Generating Linguistic Fuzzy Rules for Pattern Classification with Genetic Algorithms.- Tutorials.- Data Mining for Robust Business Intelligence Solutions.- Query Languages for Knowledge Discovery in Databases.- The ESPRIT Project CreditMine and its Relevance for the Internet Market.- Logics and Statistics for Association Rules and Beyond.- Data Mining for the Web.- Relational Learning and Inductive Logic Programming Made Easy.