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Produktbild: Knowledge Discovery in Databases: PKDD 2005
Band 3721

Knowledge Discovery in Databases: PKDD 2005 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005, Proceedings

Aus der Reihe Lecture Notes in Computer Science

97,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

26.09.2005

Herausgeber

Alípio Jorge + weitere

Verlag

Springer Berlin

Seitenzahl

719

Maße (L/B/H)

23,9/15,6/3,2 cm

Gewicht

1030 g

Auflage

2005

Sprache

Englisch

ISBN

978-3-540-29244-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

26.09.2005

Herausgeber

Verlag

Springer Berlin

Seitenzahl

719

Maße (L/B/H)

23,9/15,6/3,2 cm

Gewicht

1030 g

Auflage

2005

Sprache

Englisch

ISBN

978-3-540-29244-9

Herstelleradresse

Springer-Verlag GmbH
Heidelberger Platz 3
14197 Berlin
Deutschland
Email: sdc-bookservice@springer.com
Url: www.springer.com
Telephone: +49 30 827870
Fax: +49 30 8214091

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  • Produktbild: Knowledge Discovery in Databases: PKDD 2005
  • Invited Talks.- Data Analysis in the Life Sciences — Sparking Ideas —.- Machine Learning for Natural Language Processing (and Vice Versa?).- Statistical Relational Learning: An Inductive Logic Programming Perspective.- Recent Advances in Mining Time Series Data.- Focus the Mining Beacon: Lessons and Challenges from the World of E-Commerce.- Data Streams and Data Synopses for Massive Data Sets.- Long Papers.- k-Anonymous Patterns.- Interestingness is Not a Dichotomy: Introducing Softness in Constrained Pattern Mining.- Generating Dynamic Higher-Order Markov Models in Web Usage Mining.- Tree 2 – Decision Trees for Tree Structured Data.- Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results.- Cluster Aggregate Inequality and Multi-level Hierarchical Clustering.- Ensembles of Balanced Nested Dichotomies for Multi-class Problems.- Protein Sequence Pattern Mining with Constraints.- An Adaptive Nearest Neighbor Classification Algorithm for Data Streams.- Support Vector Random Fields for Spatial Classification.- Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication.- A Correspondence Between Maximal Complete Bipartite Subgraphs and Closed Patterns.- Improving Generalization by Data Categorization.- Mining Model Trees from Spatial Data.- Word Sense Disambiguation for Exploiting Hierarchical Thesauri in Text Classification.- Mining Paraphrases from Self-anchored Web Sentence Fragments.- M2SP: Mining Sequential Patterns Among Several Dimensions.- A Systematic Comparison of Feature-Rich Probabilistic Classifiers for NER Tasks.- Knowledge Discovery from User Preferences in Conversational Recommendation.- Unsupervised Discretization Using Tree-Based Density Estimation.- Weighted Average Pointwise Mutual Information for Feature Selection in Text Categorization.- Non-stationary Environment Compensation Using Sequential EM Algorithm for Robust Speech Recognition.- Hybrid Cost-Sensitive Decision Tree.- Characterization of Novel HIV Drug Resistance Mutations Using Clustering, Multidimensional Scaling and SVM-Based Feature Ranking.- Object Identification with Attribute-Mediated Dependences.- Weka4WS: A WSRF-Enabled Weka Toolkit for Distributed Data Mining on Grids.- Using Inductive Logic Programming for Predicting Protein-Protein Interactions from Multiple Genomic Data.- ISOLLE: Locally Linear Embedding with Geodesic Distance.- Active Sampling for Knowledge Discovery from Biomedical Data.- A Multi-metric Index for Euclidean and Periodic Matching.- Fast Burst Correlation of Financial Data.- A Propositional Approach to Textual Case Indexing.- A Quantitative Comparison of the Subgraph Miners MoFa, gSpan, FFSM, and Gaston.- Efficient Classification from Multiple Heterogeneous Databases.- A Probabilistic Clustering-Projection Model for Discrete Data.- Short Papers.- Collaborative Filtering on Data Streams.- The Relation of Closed Itemset Mining, Complete Pruning Strategies and Item Ordering in Apriori-Based FIM Algorithms.- Community Mining from Multi-relational Networks.- Evaluating the Correlation Between Objective Rule Interestingness Measures and Real Human Interest.- A Kernel Based Method for Discovering Market Segments in Beef Meat.- Corpus-Based Neural Network Method for Explaining Unknown Words by WordNet Senses.- Segment and Combine Approach for Non-parametric Time-Series Classification.- Producing Accurate Interpretable Clusters from High-Dimensional Data.- Stress-Testing Hoeffding Trees.- Rank Measures for Ordering.- Dynamic Ensemble Re-Construction for Better Ranking.- Frequency-Based Separation of Climate Signals.- Efficient Processing of Ranked Queries with Sweeping Selection.- Feature Extraction from Mass Spectra for Classification of Pathological States.- Numbers in Multi-relational Data Mining.- Testing Theories in Particle Physics Using Maximum Likelihood and Adaptive Bin Allocation.- Improved Naive Bayes for Extremely Skewed Misclassification Costs.- Clustering and Prediction of Mobile User Routes from Cellular Data.- Elastic Partial Matching of Time Series.- An Entropy-Based Approach for Generating Multi-dimensional Sequential Patterns.- Visual Terrain Analysis of High-Dimensional Datasets.- An Auto-stopped Hierarchical Clustering Algorithm for Analyzing 3D Model Database.- A Comparison Between Block CEM and Two-Way CEM Algorithms to Cluster a Contingency Table.- An Imbalanced Data Rule Learner.- Improvements in the Data Partitioning Approach for Frequent Itemsets Mining.- On-Line Adaptive Filtering of Web Pages.- A Bi-clustering Framework for Categorical Data.- Privacy-Preserving Collaborative Filtering on Vertically Partitioned Data.- Indexed Bit Map (IBM) for Mining Frequent Sequences.- STochFS: A Framework for Combining Feature Selection Outcomes Through a Stochastic Process.- Speeding Up Logistic Model Tree Induction.- A Random Method for Quantifying Changing Distributions in Data Streams.- Deriving Class Association Rules Based on Levelwise Subspace Clustering.- An Incremental Algorithm for Mining Generators Representation.- Hybrid Technique for Artificial Neural Network Architecture and Weight Optimization.