Johannes Fürnkranz / Tobias Scheffer / Myra Spiliopoulou (eds.)

Knowledge Discovery in Databases: PKDD 2006

10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Berlin, Germany, September 18-22, 2006, Proceedings
Herausgegeben:Fürnkranz, Johannes; Scheffer, Tobias; Spiliopoulou, Myra

• Broschiertes Buch

Produktbeschreibung

The two premier annual European conferences in the areas of machine learning anddatamining havebeencollocatedeversincethejointconferenceinFreiburg, Germany,2001. The European Conference on Machine Learning wasestablished 20 years ago, when the ?rst European Working Session on Learning was held in Orsay, France, in 1986. The conference is growing, and is more lively than ever. The European Conference on Principles and Practice of Knowledge Discovery in Databasescelebratesits tenth anniversary;the ?rst PKDD took place in 1997 in Trondheim, Norway. Over the years, the ECML/PKDD series has evolved into one of the largest and most selective international conferences in these areas, the only one that provides a common forum for the two closely related ?elds. In 2006, the 6th collocated ECML/PKDD took place during September 18-22, when the Humboldt-Universität zu Berlin hosted the 17th European Conference on Machine Learning (ECML) and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD). The successful model of a hierarchical reviewing process that was introduced last year for the ECML/PKDD 2005 in Porto has been taken over in 2006. We nominated 32 Area Chairs, each of them responsible for several closely related research topics. Suitable areas were selected on the basis of the submission s- tistics for ECML/PKDD 2005 to ensure a proper load balance among the Area Chairs. For the ?rst time, a joint Program Committee was nominated for the two conferences, consisting of 280 renowned researchers,mostly proposed by the AreaChairs.

Produktdetails

• Lecture Notes in Computer Science 4213
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 11871637, 978-3-540-45374-1
• 2006
• Seitenzahl: 688
• Erscheinungstermin: 15. September 2006
• Englisch
• Abmessung: 235mm x 155mm x 37mm
• Gewicht: 948g
• ISBN-13: 9783540453741
• ISBN-10: 3540453741
• Artikelnr.: 20946731

Inhaltsangabe

Invited Talks.- On Temporal Evolution in Data Streams.- The Future of CiteSeer: CiteSeerx.- Learning to Have Fun.- Winning the DARPA Grand Challenge.- Challenges of Urban Sensing.- Long Papers.- SD-Map - A Fast Algorithm for Exhaustive Subgroup Discovery.- Decision Trees for Hierarchical Multilabel Classification: A Case Study in Functional Genomics.- Clustering Scientific Literature Using Sparse Citation Graph Analysis.- VOGUE: A Novel Variable Order-Gap State Machine for Modeling Sequences.- Don't Be Afraid of Simpler Patterns.- An Adaptive Prequential Learning Framework for Bayesian Network Classifiers.- Adaptive Active Classification of Cell Assay Images.- Learning Parameters in Entity Relationship Graphs from Ranking Preferences.- Detecting Fraudulent Personalities in Networks of Online Auctioneers.- Measuring Constraint-Set Utility for Partitional Clustering Algorithms.- Discovery of Interesting Regions in Spatial Data Sets Using Supervised Clustering.- Optimal String Mining Under Frequency Constraints.- k-Anonymous Decision Tree Induction.- Closed Sets for Labeled Data.- Finding Trees from Unordered 0-1 Data.- Web Communities Identification from Random Walks.- Information Marginalization on Subgraphs.- Why Does Subsequence Time-Series Clustering Produce Sine Waves?.- Transductive Learning for Text Classification Using Explicit Knowledge Models.- Exploring Multiple Communities with Kernel-Based Link Analysis.- Distribution Rules with Numeric Attributes of Interest.- Tractable Models for Information Diffusion in Social Networks.- Efficient Spatial Classification Using Decoupled Conditional Random Fields.- Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data.- An Attacker's View of Distance Preserving Maps for Privacy Preserving Data Mining.- A Scalable Distributed Stream Mining System for Highway Traffic Data.- K-Landmarks: Distributed Dimensionality Reduction for Clustering Quality Maintenance.- The Discrete Basis Problem.- Evaluation of Summarization Schemes for Learning in Streams.- Efficient Mining of Correlation Patterns in Spatial Point Data.- Improving Functional Modularity in Protein-Protein Interactions Graphs Using Hub-Induced Subgraphs.- Refining Aggregate Conditions in Relational Learning.- Measuring to Fit: Virtual Tailoring Through Cluster Analysis and Classification.- RIVA: Indexing and Visualization of High-Dimensional Data Via Dimension Reorderings.- Distributed Subgroup Mining.- Network Flow for Collaborative Ranking.- Short Papers.- Finding Hierarchies of Subspace Clusters.- Integrating Pattern Mining in Relational Databases.- Discovering Patterns in Real-Valued Time Series.- Classification of Dementia Types from Cognitive Profiles Data.- When Efficient Model Averaging Out-Performs Boosting and Bagging.- Peak-Jumping Frequent Itemset Mining Algorithms.- Autonomous Visualization.- Naive Bayes for Text Classification with Unbalanced Classes.- Knowledge-Conscious Data Clustering.- On the Lower Bound of Reconstruction Error for Spectral Filtering Based Privacy Preserving Data Mining.- Frequent Pattern Discovery Without Binarization: Mining Attribute Profiles.- Efficient Name Disambiguation for Large-Scale Databases.- Adaptive Segmentation-Based Symbolic Representations of Time Series for Better Modeling and Lower Bounding Distance Measures.- A Feature Generation Algorithm for Sequences with Application to Splice-Site Prediction.- Discovering Image-Text Associations for Cross-Media Web Information Fusion.- Mining Sequences of Temporal Intervals.- Pattern Teams.- Compression Picks Item Sets That Matter.- Discovering Overlapping Communities of Named Entities.- Closed Non-derivable Itemsets.- Learning a Distance Metric for Object Identification Without Human Supervision.- Towards Association Rules with Hidden Variables.- A Data Mining Approach to the Joint Evaluation of Field and Manufacturing Data in Automotive Industry.- Incremental Aspect Models for Mining Document Streams.- Learning Approximate MRFs from Large Transaction Data.- Similarity Search for Multi-dimensional NMR-Spectra of Natural Products.