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

EAN (ISBN-13): 9783540664901
ISBN (ISBN-10): 3540664904
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 1999
Herausgeber: Springer Berlin
616 Seiten
Gewicht: 0,918 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2007-04-29T16:49:20+02:00 (Berlin)
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ISBN/EAN: 9783540664901

ISBN - alternative Schreibweisen:
3-540-66490-4, 978-3-540-66490-1
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: jan still, rauch
Titel des Buches: czech republic, knowledge discovery data mining, lecture notes data mining, 1999, discovery science, republic com, principles data mining, proceedings the third european conference, proceedings artificial intelligence conference

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Daten vom Verlag:

Autor/in: Jan Zytkow; Jan Rauch
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Principles of Data Mining and Knowledge Discovery - Third European Conference, PKDD'99 Prague, Czech Republic, September 15-18, 1999 Proceedings
Verlag: Springer; Springer Berlin
593 Seiten
Erscheinungsjahr: 1999-09-01
Berlin; Heidelberg; DE
Sprache: Englisch
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
Available
XIV, 593 p.

BC; Hardcover, Softcover / Informatik, EDV/Informatik; Datenbanken; Verstehen; Data Mining; Information Extraction; Intelligent Data Analysis; Text Mining; database; knowledge; knowledge discovery; logic; Database Management; Artificial Intelligence; Information Storage and Retrieval; Multimedia Information Systems; Probability and Statistics in Computer Science; IT in Business; Künstliche Intelligenz; Informationsrückgewinnung, Information Retrieval; Data Warehousing; Grafische und digitale Media-Anwendungen; Mathematik für Informatiker; Wahrscheinlichkeitsrechnung und Statistik; Wirtschaftsmathematik und -informatik, IT-Management; Unternehmensanwendungen; EA

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.
Includes supplementary material: sn.pub/extras