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Principles of Data Mining and Knowledge Discovery 5th European Conference, PKDD 2001, Freiburg, Germany, September 3-5, 2001 Proceedings

Aus der Reihe Lecture Notes in Computer Science

97,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.08.2001

Abbildungen

DXXXII, 514 p.

Herausgeber

Luc de Raedt + weitere

Verlag

Springer Berlin

Seitenzahl

514

Maße (L/B/H)

23,5/15,5/2,9 cm

Gewicht

797 g

Auflage

2001

Sprache

Englisch

ISBN

978-3-540-42534-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

23.08.2001

Abbildungen

DXXXII, 514 p.

Herausgeber

Verlag

Springer Berlin

Seitenzahl

514

Maße (L/B/H)

23,5/15,5/2,9 cm

Gewicht

797 g

Auflage

2001

Sprache

Englisch

ISBN

978-3-540-42534-2

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Principles of Data Mining and Knowledge Discovery
  • Produktbild: Principles of Data Mining and Knowledge Discovery
  • Regular Papers.- Self-Similar Layered Hidden Markov Models.- Automatic Text Summarization Using Unsupervised and Semi-supervised Learning.- Detecting Temporal Change in Event Sequences: An Application to Demographic Data.- Knowledge Discovery in Multi-label Phenotype Data.- Computing Association Rules Using Partial Totals.- Gaphyl: A Genetic Algorithms Approach to Cladistics.- Parametric Approximation Algorithms for High-Dimensional Euclidean Similarity.- Data Structures for Minimization of Total Within-Group Distance for Spatio-temporal Clustering.- Non-crisp Clustering by Fast, Convergent, and Robust Algorithms.- Pattern Extraction for Time Series Classification.- Specifying Mining Algorithms with Iterative User-Defined Aggregates: A Case Study.- Interesting Fuzzy Association Rules in Quantitative Databases.- Interestingness Measures for Fuzzy Association Rules.- A Data Set Oriented Approach for Clustering Algorithm Selection.- Fusion of Meta-knowledge and Meta-data for Case-Based Model Selection.- Discovery of Temporal Patterns.- Temporal Rule Discovery for Time-Series Satellite Images and Integration with RDB.- Using Grammatical Inference to Automate Information Extraction from the Web.- Biological Sequence Data Mining.- Implication-Based Fuzzy Association Rules.- A General Measure of Rule Interestingness.- Error Correcting Codes with Optimized Kullback-Leibler Distances for Text Categorization.- Propositionalisation and Aggregates.- Algorithms for the Construction of Concept Lattices and Their Diagram Graphs.- Data Reduction Using Multiple Models Integration.- Discovering Fuzzy Classification Rules with Genetic Programming and Co-evolution.- Sentence Filtering for Information Extraction in Genomics, a Classification Problem.- Text Categorization and Semantic Browsing with Self-Organizing Maps on Non-euclidean Spaces.- A Study on the Hierarchical Data Clustering Algorithm Based on Gravity Theory.- Internet Document Filtering Using Fourier Domain Scoring.- Distinguishing Natural Language Processes on the Basis of fMRI-Measured Brain Activation.- Automatic Construction and Refinement of a Class Hierarchy over Multi-valued Data.- Comparison of Three Objective Functions for Conceptual Clustering.- Identification of ECG Arrhythmias Using Phase Space Reconstruction.- Finding Association Rules That Trade Support Optimally against Confidence.- Bloomy Decision Tree for Multi-objective Classification.- Discovery of Temporal Knowledge in Medical Time-Series Databases Using Moving Average, Multiscale Matching, and Rule Induction.- Mining Positive and Negative Knowledge in Clinical Databases Based on Rough Set Model.- The TwoKey Plot for Multiple Association Rules Control.- Lightweight Collaborative Filtering Method for Binary-Encoded Data.- Invited Papers.- Support Vectors for Reinforcement Learning.- Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining.- Statistification or Mystification? The Need for Statistical Thought in VisualData Mining.- The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery.- Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery.