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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 / edited by Pavel BrazdilRui Camacho, Jo

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink Bücher | Lecture notes in computer science ; 3721Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005Description: Online-Ressource (XXIII, 719 p. Also available online, digital)ISBN:
  • 9783540316657
Subject(s): Genre/Form: Additional physical formats: 9783540292449 | Buchausg. u.d.T.: Knowledge discovery in databases: PKDD 2005. Berlin : Springer, 2005. XXIII, 719 SDDC classification:
  • 004
  • 006.3
MSC: MSC: *68-06 | 68P15 | 68T05 | 00B25RVK: RVK: SS 4800LOC classification:
  • Q334-342 TJ210.2-211.495
DOI: DOI: 10.1007/11564126Online resources: Summary: 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.Summary: The European Conference on Machine Learning (ECML) and the European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) were jointly organized this year for the ?fth time in a row, after some years of mutual independence before. After Freiburg (2001), Helsinki (2002), Cavtat (2003) and Pisa (2004), Porto received the 16th edition of ECML and the 9th PKDD in October 3–7. Having the two conferences together seems to be working well: 585 di?erent paper submissions were received for both events, which maintains the high s- mission standard of last year. Of these, 335 were submitted to ECML only, 220 to PKDD only and 30 to both. Such a high volume of scienti?c work required a tremendous e?ort from Area Chairs, Program Committee members and some additional reviewers. On average, PC members had 10 papers to evaluate, and Area Chairs had 25 papers to decide upon. We managed to have 3 highly qua- ?edindependentreviewsperpaper(withveryfewexceptions)andoneadditional overall input from one of the Area Chairs. After the authors’ responses and the online discussions for many of the papers, we arrived at the ?nal selection of 40 regular papers for ECML and 35 for PKDD. Besides these, 32 others were accepted as short papers for ECML and 35 for PKDD. This represents a joint acceptance rate of around 13% for regular papers and 25% overall. We thank all involved for all the e?ort with reviewing and selection of papers. Besidesthecoretechnicalprogram,ECMLandPKDDhad6invitedspeakers, 10 workshops, 8 tutorials and a Knowledge Discovery Challenge.PPN: PPN: 1647646189Package identifier: Produktsigel: ZDB-2-SCS | ZDB-2-LNC | ZDB-2-SEB | ZDB-2-SCS | ZDB-2-SXCS | ZDB-2-SEB
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